{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #See MO for orig 2/10/24.  This one from MD 3-24-24\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)         \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ########################\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections only count if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     \"\"\"\n",
    "    #global borderUnits\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        trySet = set(list())\n",
    "        for U in addedList:\n",
    "            trySet = trySet.union(set(unitNbrs[U] ) )\n",
    "        tryList = list( trySet.difference(set(addedList)) )\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP and wontEnclave(UU, addedList, unitNbrs, borderUnits) :\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  MI\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use mi_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. MI has 10077331 peeps and is divided into 4805 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 13 13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 83 counties in MI .  I will assign them county Nos 0 - 82\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Building county geom for county 80\n",
      "Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 26 unpopulated coastal tracts.  Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.  Lets plot them and their parent counties\")\n",
    "for t in skipList:\n",
    "    plotPoly(tractGeom[t])\n",
    "    plotCenter(t,tractGeom[t],6)\n",
    "plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c]:\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 26 units will be axed\n",
      "here is the proposed skipList [4522, 7, 3675, 291, 751, 4507, 4602, 3130, 4318, 4554, 3980, 1722, 3252, 140, 3702, 4729, 4047, 3294, 3105, 260, 4777, 3966, 1543, 1817, 737, 331]\n",
      "here is the final skipList [4522, 7, 3675, 291, 751, 4507, 4602, 3130, 4318, 4554, 3980, 1722, 3252, 140, 3702, 4729, 4047, 3294, 3105, 260, 4777, 3966, 1543, 1817, 737, 331]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "#skipList = [1939, 1364] #...  #alter here if needed - 1874, 1270, 1497, 1771 create contiguity problems if axed\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 0\n",
      "removing coastal units from countyNo 1\n",
      "removing coastal units from countyNo 2\n",
      "removing coastal units from countyNo 3\n",
      "removing coastal units from countyNo 9\n",
      "removing coastal units from countyNo 10\n",
      "removing coastal units from countyNo 16\n",
      "removing coastal units from countyNo 20\n",
      "removing coastal units from countyNo 26\n",
      "removing coastal units from countyNo 31\n",
      "removing coastal units from countyNo 34\n",
      "removing coastal units from countyNo 41\n",
      "removing coastal units from countyNo 44\n",
      "removing coastal units from countyNo 47\n",
      "removing coastal units from countyNo 49\n",
      "removing coastal units from countyNo 50\n",
      "removing coastal units from countyNo 52\n",
      "removing coastal units from countyNo 54\n",
      "removing coastal units from countyNo 57\n",
      "removing coastal units from countyNo 60\n",
      "removing coastal units from countyNo 63\n",
      "removing coastal units from countyNo 65\n",
      "removing coastal units from countyNo 69\n",
      "removing coastal units from countyNo 70\n",
      "removing coastal units from countyNo 75\n",
      "removing coastal units from countyNo 79\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We appear to have some populated islands.  Separate out the main state geom in next block.\n",
      "we will scale longitudinal distance by 0.71354\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 286 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 292 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 293 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 294 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 3112 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1771 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 1772 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block identifies whole districts to be cut out of the map\n",
      "For MI my input file says to cut out 1 districts out of 13\n",
      "performing cut no 0 for state MI\n",
      "the total proposed cutPop is 778885.0 . Compare to 775179\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the cut 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your cut districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# updated NCUTDISTRICTS (district slice-out) code vs. vanillaRefine - this WORKS\n",
    "print(\"This code block identifies whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02f95c46-5073-4b9d-817c-16a57d5ebb52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    }
   ],
   "source": [
    "print(nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before skewing outcroppings.\n",
      "Of the total statePop 10077331.0 we ignore 778885.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 581 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 12 rather than original 13\n",
      "Each district will be drawn to house 774870.5 = 1/ 12 of non-cutout state pop 9298446.0 vs original= 10077331.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "working on county 60\n",
      "working on county 80\n",
      "here is the MI map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before skewing outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 11\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "Working on county  60 distances\n",
      "Working on county  80 distances\n",
      "the max LAT-scaled distance between counties is 3.13194\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 3.13194  0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  40 neighbors\n",
      "Working on county  60 neighbors\n",
      "Working on county  80 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CHANGE- do not use clipLines to find outcroppings.  Instead, find boundary counties with low neighborcounts\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9b3df93-d821-44ca-b4f4-b65d13baaffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.25 of 774870\n",
      "checking if county 10 with pop 154316.0 and 1 or 2 neighbors is less pop than 193717\n",
      "checking if county 34 with pop 1995.0 and 1 or 2 neighbors is less pop than 193717\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [10]\n",
      "1   [34, 5, 64, 8, 25, 31, 71, 56]\n"
     ]
    },
    {
     "data": {
      "image/png": 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m4Pnnn0dqairmz5+PSZMmNf/gNmppvltQUIDbb78deXl5CAsLQ0ZGBlatWoUrr7yyzTHUVDka2pqD6buGeB8lLDqkKBz7T5a7Pd6KP5o4cSImTmzYGNRm816PhzMJS9v28+mnnza7zfTp0+uNw+IpksBh92yNVrtizioo7oGGOGPHjsXYsWNbvL3H2q1w3qKxAhYuXOjmblv+xlYUWQAAgaH+W7VM2o//dQ3xAzuPFaN/J6oK8lUiY+BgsLfx172aRIFDcaOXkM9xdt/l9rZVCfm7/OxSmKNMMJjE5jcmpI0oYdGZnKIqJIQF+GU3Za0Q2JnBy7RKYBznGCNQEwTmmSohNeUdLHUMEKei7D8KkdKj7dWkhLQEVQnpzMmyGmpo6+NqxwTTcsIiCoDNruESFtQmLNp9E4LCjECVeoO1lZ6qQnWRBSk9PZuwcEWBoshQ7LLjWnZeFBlcVly3XcvrrZdhs1hRXnQKwRFRrmWy7Lg+81jnfmQ7FFmGbLc5r+1Q7HbIdjvsdhvsNjsYAyTJAFGSIEoSBEmCKIoQJAmC6FxWey2JEMXabepuazhrnQjJaEJYXLzmpqNQEyUsOlJhsSNIp2Om1MVlGSUrvnbeqTt8+dkjmzZc7qqfP9ew53WW7yvaiwprRf2NOM58wfDasWsaG5WUNbjPnEP4l1VaEaUcwZSv3m4wRYJrYNW6+zxrVwITIDDHfEMCaofYFyAyAQJjEAUBAgTEl59ApN0MLkjgggQIBkA0AIIBTDSAiUYw0QBRMkIQDRBFAwTB8UUriY5rUXB8EUuiAQbRAIMkQRSAU5WBCDaUIS/vq/pBMlYn9kZeg7PXsbrPuO46DhRmA/bqM3s5O8PjvM4RaofRP3N9ZnD+Ouuc21ptjnmNWO7PsP54yrlYcfS8cb5PUBTXtAuuCzg4V5yD5jrW176vVRXlOC2nwy61oO1YI9lqyxNYx4bBdgbOJXz7+iLn48/+TPN6T7vR9bXrFAWc1144inP/RGXRccSfd6njRK8o4IrsTCbs4IoCa40N1koLMr/fiO3fKZBlRyIgO5OAukmG47GOx3PZuR9Zrr/ced0emTwTRDBBABMECJIBTBTBRAmCKEIQJcdtyXEfgCOxscvgsh2KbHe+JjK43e66dsXvhjGPPIX0wW2bosOf6P/s5kf25ZfhguSGs8TqTfTUB2A/efLMgnrJQeMneleSweotbHIfG3b9guu73VBvOQc/6xdRw2SF1x0OtLbrae28R4IAO+fYk16EoZKp7maOa+fZhdddeNZyWXGcVGWFO4fXV2DnChTOYZcVKFAgKwoULEKFfBOYzQ5BqYHAKyEodgjcDoHLELkdovNaggyBKxCguK6ZY3IagDv2aeUKan/P3wwgOLgav33X+ISGnqAwwNj5qNf2DwAhxxfCuNfu1mM4PzMnEerMT2Ridgi2CSixT/ZojE05acnBgW3/a2KLhp9P1ugdwXGHCWBgUGTH+1pw+FcwJjiWCyIYE85cBBGB4QbIigJRFGE0GiGK4pmLZIAkic4kwHkRBAiiCCaIECXJkTC41jmumSg4bkuSc5ngWs9c2wmu5KJ2vwcyt2Dz8k/AGMMVU+5H1wGD6u2biY7HOeL3TqkGV5yJm93mvHaU1tQtxald9t+ZT6CmoqL5nRIXSlh0osJih0kS/aJ4UYqIgBTRDonZ6Q5I632JV3b9TDuMYn7PogV4/cHHPb7f2mSJwV4nz6szaWCdkoz663gj69D4upO/47sVMzE8fi54TDfHqjpJJwecJ9s6iYPzPq8drpk5b9R9HK/zEKsIQ+8oyI4ZIAHBeSITBFdyCcYcY7a47uOcf2MlMzuiPLoYnR69vAWvomd0AnAhbm+34/mqtR++i6xV38NgCsBNs+YhrnOX5h/kBUwQIAkCYGi+m7dgMEH2Ym9EPaKERSf+OFGKC1Op7Yon+UPy1xqMMUgiA2BsdttWMzqqVQzByUBMH+8dx4Po09L+ZLsNn82ajrwD+xAWG49b576OgOAQtcNqEUGSINspYXEHJSw6kF9ag9jQADrBEv0Qnb9QZW19oWu3Ca/2lBWexiczpqGqtASdLxiAcU/OhCBop3ckJSzuo4RF4zjnyD5diUFp1LWQ6JG2UgD60dA+juzJwtfzZkG22zH4hlsx6Pqb1Q7JbYIoQba5137K31HConGZR4pxfnK42mEQ4ll66PtNvGL7N59j07IlEEQJE6bPQufz+6sdUqsIkoFKWNyknfIz0sBvx0uQEh2IAAONMkn0prakQksJCwf34ZYss2bNAmOs3iU9PV3tsFqMc45v/zUXm5YtQUBICO6cv0CzyQoAMJGqhNxFJSwapCgc27KLcF5cMKKCaQ4PokNUwuIVPXv2xJo1a1z3JUkbpwBrTTWW/eMxFB7PQWxKGia+8E9IRi82+m4HgkRVQu7SxqeVuJRW2/DHiVIMSI1sMOgYIfqhvRIW3y1bOUOSJMTHx6sdhluK8k5g2d8fg6WqEj2GXoHRUx9TOySPEA0G2GxWtcPQFDrjacjBgnIcLazE4C7RlKwQfaMSFq84cOAAEhMT0blzZ0yaNAnHjh1TO6QmHdqxDUsefwCW6ipcfue9uklWAECUJNiohMUtVMKiAbVVQGmxQegSq40xBghpG3bWcPpaUGeQOh80cOBALF68GN26dUNeXh5mz56NSy65BL///jtCQnzve2Xz58uw5YtlECUDbnp2Djr2aIfRFtuRZDDCbqUSFndQwuLjbLKCLYcKMSgtikpViP9gzDUNkLb4bsIyevRo1+2MjAwMHDgQnTp1wvLlyzFlyhQVI6tPURR88+qLOLxzGwLDwnHr3DcQEqW/YRskgwF2GunWLZSw+LhdR4sxpEs0BMF3vwgJ8TzttWHRmvDwcJx33nk4ePCg2qG4WKqq8Mnfp6E4LxfxXc7DzbNfhig1P8y9FkkGCZbqarXD0BT6ye7DrHYFgUaJkhXif9rYhmXu3LkYMGAAQkJCEBsbi/Hjx2Pfvn31trnssssadPO97777PBO3BlRUVODQoUNISEhQOxQAwOmco3j/gckozstF7ytGYtKL/9JtsgIABqORSljcRAmLD6uw2BFqpkIw4o/aVsKyYcMGTJ06FVu3bsXq1aths9kwYsQIVFZW1tvu7rvvRl5enuvyyiuvtDFu3/XEE09gw4YNOHLkCDZv3owJEyZAFEVMnDhR7dCwb+svWPrUQ7DW1GD436ZixL0PqR2S1xmMBsh2anTrDkpYfFhEoAFFldQoSw8sFgsefPBBdO3aFb1798att97a5HJfEx0d7SqFePnllwEA27dvhyAIrkvt+p07d7b9gG0sYVm5ciXuuOMO9OzZE3369MHixYtx7NixBrEFBgYiPj7edQkNDW1r5D7r+PHjmDhxIrp164Ybb7wRUVFR2Lp1K2JiYlSNa9N/l+C71+dCECXcPPtl9LlydPMP0gFJojYs7qKf7z6MMQZZoTp8PZg+fToYY9i/fz8YY8jPz29yua+ZNGkSrrzySlx99dWuZRdeeCEURXHd7969Ow4fPox+/fp54IiebcNSWloKAIiMrD+j+SeffIKPP/4Y8fHxuPrqqzFz5kwEBgZ65Ji+5tNPP1U7hHq4ouCrec/hyK+7ERQRiVvnzUdwuP/MOC8ZDagpOoU1C9+FItshywoUWYbsvCiyDEVWHLcVx23HtQyuOJZzRUZwWBhuePpZzQ+k1xJtKmGZN28eGGOYNm1ag3Wcc4wePRqMMXz99ddN7odzjmeffRYJCQkwm80YPnw4Dhw40JbQdCMiyIjCCovaYZA2qKysxMKFC/Hiiy+6JseLj48/53Jf9MYbb2Ds2LFNbrN3795mt2kxD47DoigKpk2bhosvvhi9evVyLb/lllvw8ccfY926dZgxYwY++ugjny3h0pvq8nL85+G7ceTX3Ujs1gN3v/WhXyUrAJDUvTdMYRE49PvvOLp/P04cPoyTx4+j6ORJlBcXo6qiEjarFQCHZDAgINCMkLBwRMTGIiYxER1SUhAaEozc37NwOueo2k+nXbS6hCUzMxMLFixARkZGo+vnz5/f4plLX3nlFbz55ptYsmQJUlNTMXPmTIwcORJ//vknAgICWhuiLqTFBDu6NdMQ/Jp16NAhREZG4qWXXsKaNWtgNpsxa9YsxMTENLp82LBhaofstqeeegoAsGTJEg/t0XMlLFOnTsXvv/+On3/+ud7ye+65x3W7d+/eSEhIwLBhw3Do0CGkpaW5fRwGDl/u1uwrTmYfwmfPPQ2bpQZ9R47BsLvuVzskVXQdOBhdBw5u0z5O5xzFkiemQpFlD0Xl21pVwlJRUYFJkybhgw8+QERERIP1WVlZeO211/Dhhx82uy/OOebPn49nnnkG48aNQ0ZGBpYuXYrc3NxmS2b8RY+EUPyRW6p2GKSV7HY7jh49ih49emDHjh148803cdNNN6G6urrR5SdPnlQ7ZLctWLAA8fHxCA4O9swOPVTC8uCDD+K7777DunXr0LFjxya3HThwIAD4VDdfvfnrlw345O+Pwm61YMR9D/ttsuIpjDlO4bxO1ayetaqEZerUqRgzZgyGDx+OOXPm1FtXVVWFW265BW+//XaLirezs7ORn5+P4cOHu5aFhYVh4MCB2LJlC26++eYGj7FYLLBYzlSTlJWVteZpaEZYoAFHCjmqrTLMRpqZWWuSk5MhCAImTZoEADj//PORmpqKnJycRpfv2bMHcXFxaobslkOHDqGsrMzDPWzaVsLCOcdDDz2EFStWYP369UhNTW32MVlZWQDQpm6+NdXVOPLXH2CMQWACmMAAV4Nk0TEenuBYzgQR0XHxEET/+JvetGwxtn/zBSSjETc+Ow8JXc9TOyTNY4IzYeGUsDTq008/xa5du5CZmdno+kcffRSDBw/GuHHjWrS/2kaGZ39Bx8XFnbMB4ty5czF79mw3ota+jI5h2J5dhIGd9Tfio95FR0dj2LBhWLVqFa666ipkZ2cjOzsbgwYNanR59+7d1Q7ZLbfffjtEUcS9997ruZ22sYRl6tSpWLZsGb755huEhIS4vkvCwsJgNptx6NAhLFu2DFdddRWioqLw22+/4dFHH8XQoUPPWc3dHFkQkV9twU+ffd7ixyQEm3HvE0+36nhawRUFK16ejeysnQiKiMRt895EUHi42mHpAnOO0cX9ZM4ttxKWnJwcPPLII1i9enWjbUu+/fZb/PTTT9i9e7fHAmzMjBkz8NhjZybBKisrQ1JSklePqTbGGKJDTCgor0FsiH+369Gi9957D1OmTMHTTz8NQRCwYMECdOjQ4ZzL28rxBea5L7GIiAiUlJQAcPRsmj59uutLcuvWrRg8uG118efWuufw7rvvAnAMDlfXokWLcMcdd8BoNGLNmjWYP38+KisrkZSUhOuuuw7PPPNMqyNlgojwkDCMuuBScK6Acw6uON4HzhWAOxoAg3NwzvHL1q2orqlp9fG0oKaqAp9MfxQlJ/OQeF46bnh2LiSDfgeDa29UJdSEnTt3oqCgABdccIFrmSzL2LhxI9566y3cf//9OHToEMLPyp6vu+46XHLJJVi/fn2DfdZWG508ebJeUezJkyfRt2/fRuMwmUwwmfyvEWpaTDC2ZxdRwqJBnTt3xrp161q8vK04OJgHG4AWFxefc53sjQZ/bSxhae4XZ1JSEjZs2NCqfZ8TA4JDQ5Fx2eUt2nzHjkxdT0Z96tgRfDrzSVhrqpExbCSG3/1giztikJYRaquE/GT4C7cSlmHDhmHPnj31lt15551IT0/H008/jejo6AbFwr1798brr79eb/yGulJTUxEfH4+1a9e6EpSysjJs27YN999PDbLOlhRpxvHiKnSM0OdYEYQ4+MNcQvo9ee/bvBHf//tVcM5x5d+mIsNPBoNrd6y2Ssg/Sljc6iUUEhKCXr161bsEBQUhKioKvXr1Qnx8fIP1gKPRYd1Gb+np6VixYgUAuMZxmTNnDr799lvs2bMHt99+OxITEzF+/HjPPVOdSAgz43QFjX5Lmqb5Om0N/hJ3njrcegzXYUK28eNF+O6NV5wj177iE8nKu+++i4yMDISGhiI0NBSDBg3Cjz/+6Fr//vvv47LLLkNoaCgYY67qT1/nasNCVULes2/fPtfIk4BjDIfKykrcc889KCkpwZAhQ7By5Uq/H4PlXGps/tHnnrSeo0pIyzw3cFx7cidcBuiqAEmRZXw59zkc25PlGLl27nwER/jGYHAdO3bEvHnz0LVrV3DOsWTJEowbNw67d+9Gz549UVVVhVGjRmHUqFGYMWOG2uG2mCA4ephp/gdKC7U5YWmsXUpdjb2QZy9jjOH555/H888/39Zw/IJB1PapiHif5n+5M41WCblTMsSguad3LlVlJfhkxqMoO30Kied1x43PveRTMy2f3SThxRdfxLvvvoutW7eiZ8+ertHamzuf+ZraNkEKlbAQXxVolFBeY0NIgO98IRAfw+HRRrftT5slLO5gYFB0kLHk7t+Lz1/4B+xWC/oMH41hf3vApxvXyrKMzz//HJWVlRg0aJDa4bSNn7VhoYRFg9LjQ5CVU4LzkxuOMkwIoKcSFm1xP2ptv09Z//cD1n74LhiAkfdPQ6/Lhjf7GLXs2bMHgwYNQk1NDYKDg7FixQr06NFD7bDapLZKCNRLiPgqxpjGv+a0Qcv1wrJs13T5CrT6i9HNKiGtPk3OOVa9Ox9/bFgLQ4AZN89+GbEpndUOq0ndunVDVlYWSktL8cUXX2Dy5MnYsGGDppOW2ka3VCVEfJqmT0bE6xRdVDZAWyUtbia4Gnpm9dgsFvx35pM4dfQwwhM64NYXX4MpyENzSHmR0WhEly5dAAD9+vVDZmYm3njjDSxYsEDlyFqPUZUQ0QJfriPWiwpbhdohtJosy/pow6Ix7rzmAhNQoSiY9eyzADiYM+FhrsSHg3HnNeBIiJjjGOGhobhoyBCcP+hi1+Bh7aEo7wT++4/HUVNZgS4XDsY1j053zWejNYqi1JuTTovOzCWki58nzaKEhZBz2Fu0V+0QWk2Boq3SiXPR8RfxmAkTkLV9OzhXoCgKuHPofs45FO4Yzl9xDu/PncP5K+CwW204VVKC/61ei5WrViGjR3eMvPZ6GI3eHf37YOYW/O/1eVBkGUNvuRMDxl3n1eN50owZMzB69GgkJyejvLwcy5Ytw/r167Fq1SoAjjnt8vPzXTN179mzByEhIUhOTkZkpG90zW4MDc1PNMFqV2CTFRhEbf660YL+cf3VDqHVZEXjY/VoNdlyI+y0Hr2Q1qNXqw4jy3Zs/2kttm7Zgp1/7UfWnDno17snRoy/zitz9fz836XY9vVyiAYDrv/78+jUq4/Hj+FNBQUFuP3225GXl4ewsDBkZGRg1apVuPLKKwE45vqqO6Hu0KFDAZyZe8pX0cBxRBNiQ0worrQiNpQG1yMN2WWbRitVammzW3N7VcOJooRBV47EoCtH4s8d27Hqh++x/Y+92LXneQzo2wfDrxkPUWr713vdweACwyNw67z5CInQ3ozxCxcubHL9rFmzMGvWrPYJxoOoSohoQqeoQGTllFDCQhqlQIFW24EQ9/TofyF69L8Qv23djNWrVmLLb79jR9avuKh/P1w+9ppWt3GpLC3FJzMeQXnhaXRI74EbZr7kkSSIeA5VCRFNoK7N3qflhs2KLGu3WgXQ8Ei36h0646LByLhoMHb/vBFr1qzGpl1Z2LZjJwYPvBBDR49xK3HJ3fcXPp/zD9itVvQbMx6X3f43L0ZOWot6CRHN0PDpSBO0XMzqKGHRMi1WCXH4wl/l+UOGou/Fl2DnxnX46ad1WJ+5E1u2b8eQwYNx8ZUjm01cslZ9j7WL3gNjDGMeeQrpg4e2U+SkNZgggNPAccTXJUcG4mhhJTpFBakdCvExskJVQu2NcTtOnipEitqBwPHLu/+lV6Df0Mux7afV2LBhI9Zu2YZfNm/GpUOH4qIrhjdagvjDW6/hr03rYAwMxMQXXkV0x2QVoiduYYyqhIjviwo2Iae4Gp201waOeBnnXNvjsGiwSkgABwTf+kpljOGiYSNw4eXDsXn1Smz6ZTNWbfoFmzZtwuVXXIEBQy8D4BgMbtk/HsPpnKOI7JCMSS/9C8YAah+nBYwJVCVEiL/TdBsWRdZ4AYv2qoQEcMTExasdRqMEQcCQkVdh8JWjsPHH77F52zZ8/9N6rF+3DoP798fuLz6GpaoS3S4eijEPPanpz77fYYKmq6/dQQkLITpk5zZtl7C4ePGL+PQBoCwXEESAiY7rurddyySACWdu172IBkA0AoIIAQo4E70XrwcIgoDLxlyNoaOuwrrvvsXmbduwdaljaPrLJ9+NC64ap3KExF1MoCohQoiGKZwGjmvOr6+9gHJL20cx5WBgTIGRjUc4fDthqSWIIoaNm4D4hAT87z95kE1mnDxVCEtVFUyBgWqHR9xBbVgIIbIiY2/RXjAwCEyAwAQwxiAyEQIct2uXu9Y7t2VgYMx5qbMMgGtZ7TXgHHDMOU9M3XWtLZpXeNt6rHDOodjtULgMRZahcBmy3XGtcDsUWYGiyFBkxzZccQwvX7s9V2QoXIEs28G54rgoHLJiB1cUcChQFDsUhTvuK7JzP44h6blsQ4Cdo2j9J+C7foIgGQBRdFxLBoiSAezILxDsVeC9rwUTGARRBBNEMFGEwBy3BVE8UzrCBEepifN+uSUSg0cEQRhwF6DIAJcd14rdeVtxXtsbrpdtzuV2KLYaKLYafPNdHmxxF+C8Vr/q7a/nhRfh/R83I9lWjF07d2D3zh245PIrMGjsOK+MmEs8j1GVECFkfJfx2F+8H7Iig4ND4QoU7pzrBYrrfmPLOOfg4K5rhSuu+wDqrePgcPxff1ndLyF3E5eaqgp0L6jBj/8cAwAQmILzbNnYK6W1fCfMNe2e4w6cY//Ujv3gXMdZI9uAObdjju0Y6t/mzLlN7bbCmX1zx/OtKu6FI2YJqHC+LgoHV2yAYgMUDmbpDA4B8qnjjtePM8cFzmsuOKPhjdYs2bgJQspgIMqN16QRgvNSvOZVRBtC2rQvVUhGhHbti7tHDMW3ixdh4/qf8Mv69RhxzdXoe/lwCII2So38FWMMCpWwEOLfukd1R/eo7mqH0SpyRQWKfo1BzMMP1VueqlI8rfHtw6/hgucf99wOOXeWlDhLTgDAYPbc/jVKFBgsNhmh0TG49YmnUHg8B19+uBA//u9brP3hB4y/eSK69B9IDXF9FRM01Ti9LWjmPA0rrbYh2EQ5J2nIMZiUxtuxeBpjgCgBktGRqHg6WeGufzRFFATY7Wc+K1Edk3DPs7Mw+f6pMBlN+OyTj/HmjKeQ89fvKkZJzsWfGt1SwqJhBwsq0DmaBo0jjRBFQPaPLzGfwTybrMiyjJkzZyI1NRVmsxlpaWl44YUXPN5eQRAFWGz2Bss7duuBh156GRNvmwybzYalC97Fe7Nm4vSxIx49PmkbxgSqEiK+r6TKCkGgYlrSEJWwqMOT1SYvv/wy3n33XSxZsgQ9e/bEjh07cOeddyIsLAwPP/ywx44jCQLscuOfFcYY0voNwOPn98Oejevw49df473X/olOHZIw7s67EBoT67E4SCsxRo1uiW87XWFBl9hgtcMgvopKWFTiuRPH5s2bMW7cOIwZ42g4nZKSgv/+97/Yvn27x44BAJIowNbMZ4UJAjIuG4aeQ4Zi2w/fYf3a1fj3C7OQnt4Do2+9DYGhYR6NibQcEwRHuyw/QFVCGnW0sIrmECLn5PgS849fXb7C06/24MGDsXbtWuzfvx8A8Ouvv+Lnn3/G6NGjPXocSRQhtzC5FSUDBl8zAU+8/BoGDLwIe/f+idefmYH/+3gJrDXVHo2LtBD1EiK+zGpX2mNcLUKIOzycsUyfPh1lZWVIT0+HKIqQZRkvvvgiJk2a5NHjSKKAqhr3TnjGADNGTLodQ64ehx8//giZ27dhR+Y2XDpsOC666mqIEo3h0l4YVQkRX7b7WDEGpLR9hE5CiGd58ryxfPlyfPLJJ1i2bBl69uyJrKwsTJs2DYmJiZg8ebLHjmOUROfs3u4LDA3DdQ88iLJTBfhm8SKsW7sGm35ah6vGj0fvoZc7SvqIVznaq1EJC/FBx4urEB8WQI1tCfFBnmx0++STT2L69Om4+eabAQC9e/fG0aNHMXfuXI8mLIY2JCy1QmNicduTT+N0zlF8sXAh/vf1V/i///0P42+5BWkXDKAxXLyJ+U/CQumvhlRbZeSX1lDbFdJC/lFM7Cs8fU6uqqqCcFYJhSiKHm+vYDJIUDzUQDs6qRPum/U8br/3fhgMBnz60VL8++9P41TOUY/snzTkT1VClLBoBOccO48Wo1+nCLVDIZpBv2rbk6dPGVdffTVefPFFfP/99zhy5AhWrFiBf/3rX5gwYYJHj+OoEvJs9Ende+Hhua/gpkm3oaK6Gp8v/NCj+ydnUJUQ8Tk7jxajf0oEFa0S4sM8+ef573//GzNnzsQDDzyAgoICJCYm4t5778Wzzz7ruYMAMBkN4F7oFssYQ9cBAxH+ww+w22we3z9xYswr758vooRFAw4WlKNDhBkBBpqEjLjDP4qJfQbnHv1BERISgvnz52P+/Pke22djTAbJObu3d/hTlYUaGGPgHi4h81VUJeTjTpVbYFc4EsJokjZC3CVJEoxGI8xmM8xmMx555JEml/ujAKPk1YRCYAIlLF7kGJrfP0a1phIWH2aTFRw6VYGLOkepHQrRJM/92h8xYgTy8/MhCAJCQkLw5ptv4vzzz4fFYsHjjz+OVatWISAgAH369MHHH3/sseN6wkcffYSbbrqpxcvbQos1tgFGg3cTFoFKWNzBFQV2mxU2iwV2qxV2a91rG2Sb1XHf5rhYK8uh+EkJCyUsPmzHkWIMTKXxVoj6li9fjvDwcADAihUrcMcdd+DXX3/F9OnTwRjD/v37wRhDfn6+uoGqiANgGmzo7O0SFiZov4RFUWRn0uBIHM4kE1bYLRbYbY77ruUWC+QaG2SLFYrFBrnGDsUqg1vt4DYFilUB7Apg54DMATsAGRAUwa2aXJFJsJQWIzKxg9eeuy+hhMVHHSyoQFpsEI23QnxCbbICAKWlpWCMobKyEgsXLsTx48ddbTfi4+NVivDc7rjjDkyePBnJycn45ptv0L179yaXt542T8pGo9Gr+3dUCXmnUSjnHIpsP0cy4bg+dTQbeQf3Ibn3+aiurgartoFb7JAtNsg1NigWZzJhk8GtMrid10kmACYzCHCj/SBjEEQBXAAgAFzkgMTAJAZIAgSDBClEBDMKEIwSmFGCFGCAEGCAGGCEIcAEyWSEZDLBYHBcS0YTJKPRcW0wQDQaIRkMyF5+EJfd5rmJMH0dJSw+qrjKSpMbEp9y++23Y926dQCAH374AYcOHUJkZCReeuklrFmzBmazGbNmzcKwYcNUjvSMTZs2YdCgQaiqqsLll1+OSy+9FAUFBedc3lZa7MUniiLAORRFaTDuiycwxmAtOY3Vyz6C1WKFxWaD1WaD3WaDzWaH3e64yLIMWZahKAoUpfZaAeccnNdecwDcMaQw545J/1x5Yu2Ns98DR9nXnuwcSIZAdBE7I9RW40wiBDADAzMIEE0GsBAzBJMEMcAAwWSAFGCAaDZCMpuciYMRhjrJg8FUJ5FwLhel9jutMsm/mqFSwuKDsk9XonM0DQ5HfMvSpUsBAEuWLMHTTz+NOXPm4OjRo+jRowfmzZuH3bt348orr8Qff/yBuLg4laN1GDRoEAAgMDAQCxYswPnnn9/k8rbSbMICeC1hSe2ShrzjR5C5bSsYY86LAIEJYIIAQRAgCgIESYRBFB0JgCRBFA0wGEQYDQbnxQijyYAAkxEGZ2IgGgz1koWGiYQRAIMoSQgMDcNfvx3Djp9347oHPTuWjVq4zT+6M9eihMUHlVRZkUoJC/FRkydPxn333YcOHTpAEATXZHznn38+UlNTsWfPHp9IWAoKClBdXY1OnToBAJ577jmEhoaec3mbabNGyJWw2O12SF4oHbjsuhtw2XU3eHy/rWEOMsFut6sdhsdoMUFuC/8qT9IIf/sQEt9WUlKC3Nxc1/2vv/4aUVFRiI2NxbBhw7Bq1SoAQHZ2NrKzsz3QFsQz/vzzT3Tv3h1msxkBAQHYvn07vv/++3Mu9wQt/u3WJiyyrP+usYHBAZDt+hnEjksM3EPTKmgBlbAQQppUWlqKG264AdXV1RAEATExMfjuu+/AGMN7772HKVOm4Omnn4YgCFiwYAE6dPCNHguXXXYZqqqqGl13ruVtUt4RWixm8aeEJSTYrKsxS5RKG4p+O4Wo89Uv0WwPlLAQQprUqVMnbN++vdF1nTt3djXE9WeccyD4BASxi9qhuM2fEhaT2aCrEgkeF4ig6EC1w2g3VCVECCFt5Oi9wsAE7U2f4U8Jiyhp7/1pSnz3aJw4UKh2GO2GEhZCdEt71RNaV3vy15Lahrb+kLDoTXjncMgF1WqH0W4oYfFBRlFAtZW+PAjRCi2P5OpPJSx6U3miHAg2qB1Gu6GExQelx4dgb36Z2mEQQlqoNmGhXkKkXckczOw/TVEpYfFBgsCoMJ8Q0i7qjsPiD7SXUp5bYGwglOIatcNoN5Sw+KgQk4SyGv2MF0CInlEJi3bo6ccgM0tgFf5znqCExUd1iQ3GgZMVaodBNE17J0+tooSFqIExBiXECHu5Re1Q2oX/VH5pjGO+DcAmKzCIlFcS4usszIjlv/2J/zt8DEZBgCQwGBiDQWAwCgIMggCDwCAJIiSBISQgABf176d6zyJKWLQtPCUMeb+fRtIg3xiw0ZsoYfFhfTqGY/exYvRPiVQ7FEJIEwwGA/IDU2Axchy32mBTOGQAdg7YAdg5hwwGOwCFc9RYbSg1B+Hz5AIkJCSoGjslLNoWlRyKQ9tzm99QByhh8WGiwBBgEFFpsSPIRG8VIb6KMYYAkwEv3nhVi7Y/ceIE7vhpi09UIdE4LNomRZkhFPpHw1uqa/BxPRND8UcudXEmRE8UxTE8vCCo/xVcG4OnE5YDBw7AbDa7LkajEYwxHDp0CIsWLUJwcDACAwNhMplw1VUtS/RIQ0xggKT+56g9+Mez1DDGGMLMBpRUWdUOhRDiIbXJgS8kLIwxgDGPJyxdu3ZFdXW16zJixAjExsYiLS0NDzzwAB555BFUVVXhzz//xMqVK/Htt9969PhN0VtpEmeA4geDjar/10Ka1S0+BAcKqMcQIXqhKAoYfCNhqeXtk/jq1atx1113AXAkSadOnQIAnD59GowxpKSkePX4tQRBQGWlvnrVRPePx5H1x9QOw+va1DBi3rx5mDFjBh555BHMnz8fAHDvvfdizZo1yM3NRXBwMAYPHoyXX34Z6enp59zPHXfcgSVLltRbNnLkSKxcubIt4elKkFFCeY0NIQH+MwwzaSs9jTihL4qioNwUiId/WAuRMYiA48Jw5j6rXea4LzFAAAMTGBhjEBlDmWhACBRInIPhTLdqEcC1fXoitYVJgLfb0ixYsAB2ux3PPfccAGDRokWYNGkSPvzwQ8iyjLvvvhsZGRlejaGWKEqorKhGaKh+ZjmO6hqJom15aofhda1OWDIzM7FgwYIGH7J+/fph0qRJSE5ORlFREWbNmoURI0YgOzu7ye57o0aNwqJFi1z3TSZTa0PTpe4JIdhxtBgDqMcQIZqXlJSEv+ecQJnNCJkDdq7AzgGZc8icw865o4cR55Cdy20A7AoHwKEoChTOcYoBFrsdEufgdRLUXTYg7K/9+JsbpRbenA/ptddeQ79+/RAQEAAAePLJJ3Hvvffi7bffxvr16zF8+HCMHTsW11xzjddiqCVIEior9FXCAgCyWYK9zAIpVL/nzlYlLBUVFZg0aRI++OADzJkzp966e+65x3U7JSUFc+bMQZ8+fXDkyBGkpaWdc58mkwnx8fGtCccvMMZgEAXYZQUSjctCiKaZTCZcffmlXtv/Xcu+hOBmoYm3qoTy8/Nx4MABfP/99wCAffv2IScnB2+//TYA4LLLLkN8fDy++OKLdklYJElCdZX+etXE9o7Bsd0n0fnSZLVD8ZpWnfmmTp2KMWPGYPjw4U1uV1lZiUWLFiE1NRVJSUlNbrt+/XrExsaiW7duuP/++1FYWHjObS0WC8rKyupd/EEv6jFECGkBDkB0c6Rjb5WvTJ8+HSEhIa6eQKmpqWCM4bXXXgPgSGDy8/MxZMgQL0VQn2QwoFqHnRgi0iJgP6Lv84PbCcunn36KXbt2Ye7cuefc5p133kFwcDCCg4Px448/YvXq1TAajefcftSoUVi6dCnWrl2Ll19+GRs2bMDo0aPPmfHPnTsXYWFhrktzyZBeSKIAWcPT2BNC2ocCQHCzXYpdVrwSy5dffonrr7/edd9oNGLevHmYOXMmzGYzMjIyMGbMmHql894kGiTUVOuvSoiJ+u/e7FaVUE5ODh555BGsXr3aVRfZmEmTJuHKK69EXl4eXn31Vdx444345ZdfzvmYm2++2XW7d+/eyMjIQFpaGtavX49hw4Y12H7GjBl47LHHXPfLysr8JmkRGaNqIUJIkzh3/GOznXtivLMb2iqKd34MlZeXN1j21FNP4amnnvLK8ZpjMBhg0WHCAgAsxICy42UI7Riqdihe4VbCsnPnThQUFOCCCy5wLZNlGRs3bsRbb70Fi8UCURRdJR9du3bFRRddhIiICKxYsQITJ05s0XE6d+6M6OhoHDx4sNGExWQy+W2j3G7xIdh/sgI9EvX5gSSkvTzzr1UwGTzXbiM++dylyO0tgMv4sLASSz/9pkXbRxvNsPvJKBcGkxE2i/dmOC4sLKx33qqqqsLhw4dRUFCAa6+9FkePHkVYWBgAYPLkyXj00Uc9duzOI1Jw4L97EXpnb4/t05e4lbAMGzYMe/bsqbfszjvvRHp6Op5++ulGewFxzsE5h8XS8oz2+PHjKCwsVH2ODV8UYBBRY9f/AEHEE9Qf9t2XmQwyZj6kzxFWZw+7xDXOCdB8D6B/n65A5yaGntATo8mIqsoqr+0/KioKWVlZrvuvvvoqNmzYgMhIRw/P119/HePHj/fKscUAA8QAdSfT9Ca3EpaQkBD06tWr3rKgoCBERUWhV69eOHz4MD777DOMGDECMTExOH78OObNmwez2Vxv6OX09HTMnTsXEyZMQEVFBWbPno3rrrsO8fHxOHToEJ566il06dIFI0eO9Myz1Bk6DZGWMKWn4+TceWqH0ST76dOwnTgBc58+DdaFhBvxvw8+dtxxt7aiBX8k9jj9zm6bkJDg1g8+089/QGqinaGemMxGlJaUttvxFi5c2GSbT48zS6jIq0BwQnD7HbOdeHRGvYCAAGzatAnz589HcXEx4uLiMHToUGzevBmxsbGu7fbt24fSUscHRhRF/Pbbb1iyZAlKSkqQmJiIESNG4IUXXvDbap/mCIxBVjhEd/stEr8SPmG82iG0SZyX97/jwAkvH0FDGIN3mtz6HmOAEXar96qE6tq8eTOKi4sxduxY17Lp06dj5syZ6NGjB+bOnYvOnTt79JidR6bi4NcH0G1iD4/u1xe0OWFZv36963ZiYiJ++OGHZh9Tt3jSbDZj1apVbQ3Dr3SNC8b+k+XonkDtWAghniHL/tED0RxghN1mb5djLVy4ELfffrtrRuyPPvoISUlJ4Jzj7bffxtixY/Hnn3969Jii2QBm02f66R+trHQm0Cih2kbtWAghnsHA6o2Uq2fmoAAoTfSe8pSKigosX77cNX8SAFdvVsYYHnzwQRw+fLjJMcdaS4oLRMHe0x7fr9ooYSGEEOI3VUImUURApfcTls8++wx9+vRxzaNnt9tx8uRJ1/ovv/wScXFxiIqK8vixUy5NhnXxXx7fr9o82oaFtB+jKMBqV2DU+UBBhBDvYwzw0jAsPicgxIwKk/fb/y1cuBB33323677FYsGYMWNgsVggCAKio6Px7bffeuXY3KbAGqi/07v+npGf6BYfgj9zy9AnKVztUAjRJmqzXo/M/aOMxWSWoCjer1LfvHlzvftBQUHYsWOH149by5rh+ZIbtdHPc40yiIJz5lZCCGkbxhj8ZdaPALMRXNF3ciaYJUiH9TevECUsGmYUBVhoEDlCWsdPTtAtwQC/mafMaJR0n7BwmUPpGKR2GB5HCYuG9UgMxV95DefpIIQQ9zAofpKwNDYiu94wgwDY9Pd+UhsWDRMFxwByhBDf1dTcMo8//jh++eUXmM1mBAcHY/78+RgwYIAqcSpU5KQbTGCAc1qcsye51DJKWDTO8bnU14eSED1pam6ZCRMm4IMPPoAkSfjuu+9www034MiRI+0eI2PwmzYsfkXmgKSfcwNVCWlcl9hg7DtJ1UKEaMXChQsxZcoUAMA111zjGgX1oosuwokTJ2C3t88orGfzlyohf8FEBqazYS/09Wz8UEiAAZUWdb7gCCHuaWxumVpvvPEGrrrqKlcC054Y/GccFoDaW2sVVQnpQGiAAaVVNoQFGtQOhRDShLPnlqn18ccfY/ny5di4caMqcTEwv0pYiDZRwqIDXeNCsPNoEfp1ilQ7FELIOdTOLZOZmVlv+WeffYbZs2dj7dq1iIvz9hzV58CoSkh3dJiBUpWQbuinYRUhenT23DIAsHz5cjzzzDNYs2YNkpOTVYvNUSWkvxOcP1MCDagprFY7DI+ihEUn0mKCcOhUhdphEELOoW5j21qTJk1CTU0Nxo0bh759+6Jv375emb23Wcx/xmHxF0kXJuBoZp7aYXgUVQnpRHigEQcLKoAYtSMhhDTm7LllAMBm8/6swS0RFBSIjbv+xPC0DmqHgm827kZhSRnssh02mx02ux2yLMPuvJbtMmRFbtVQDmFVMoIsMoLCwz0fuI8xBhnBrfoaCZ0SFh2JCTHhZFkN4kID1A6FEKIhPTonYfOWzOY3bAfbduzEyMsvgckowWgwIMBogNlogNlkgMl5bZRECIL7FQQV2/JgSg2DITbQC5H7Gu4YYEdHKGHRkU5RQcg8UkQJCyHELUFBZpgiotUOAwBgMgfi0vO7eWXfTBTAZf+o+jp9tBRBOkvMqA2LzsSHBiC/tEbtMAghGqJw7jM/xrkX29IwiQGyvic+rCWIgu5GL6aERWeSIgORU1yldhiEEA2RFT+Z3kNk4Hb/SFiiUsJQnaevjhhUJaRD8aEByC2pRmK4We1QCCEaoPjJfGRMFFD9RyHkEoujaohzQAG44rjNFe4Yv0RxlvRwjuDBiRBM2jtVCkEGCNX6GgVde+8CaVZSZCB2HCmihIWQJuistLxNZMV3RnLyZuIU0DUCQpDBMZux88IEAIw1uqzqt1OwF1lgTNDeqZIx5jtvqodo710gLRIVbMKpcgtiQkxqh0II8XF+U8JiEGDqFNri7QWzpOk2L+ZfCyFfY4MYpI9pW6gNi06lRgfhSGGl2mEQ4rP0f3p2j6/kK95sdOsuJmm7V5HCnA2NdYISFh2LDDLidIVF7TAIIT7OzhUwSuEaYBpvpFvTK0qT7W/OhRIWHUuLCcbhU1TKQghpmsI5BMpXGpIEQMMlLExnLbUoYdG5yCADSqqsaodBCPFhig/M7KsoCmqsNvjS4CFMFDRdwsJNEpQa/fQU0k9ZEWlUWkwwdh0rRr9OkWqHQgjxUQoHBC82Ynnn81UoOHW60XWMMVe7FVEQERnpO99VTGJQqjWcsBgFcIsMBOjjVK+PZ0HOiTGGNX8VoEdCGMxGUe1wCCGtdNJiQ6WsQAGHzB3VOAoc1zIHFHAo3NFdm3PuqgzgtRdee5/XqyjgHDhabUFFfj6+XLcDClegKBxcUZy3Adl5myuOkhDHbQ6Fc/Da7TmHrCiOY3PuXOa4X1iQh5efeqA9Xy6PYKK2q4TEEAMqi6oRGqaP3qKUsPiBp0Z2w/bsIgzsHKV2KISQVnpiXw6uiAqFCEdpiMAcdfoiY65rxgDB2XiW4UzPH+a6ONedNUTHwPROKOcyBIFBYgYIAoMoCK5r5rwWBQGMOW+LguPYouDc1lFCUm+dIEASBQQGGNvtdfIoSdtVQh17xuBoZh5CU8PVDsUjKGHxA4wxxIUG0EzOhGhYZ7MJd3bw4gSFib4x+aEvYRIDt2u3hMUUEwh2ulrtMDyGGt36iZToIBw5TT2GCCGkpRyzO2u3hIUxBmNKGPL3FKgdikdQwuJHUqODcJQGkyOEkBZhEgM0XMICAJ2GJKHs98YbPGsNJSx+JDY0AKfKaSA5QrRI26dNjdJ4t2bAMfidXj48lLD4mbjQAOSV6qdOkxBCvIVJTNNVQi4+NLZNW1CjWz9TO5NzQhjN5EyIltBAtO3P0YbFd0/2XOHgNgXcKoNbZChW2XHb6limWBz3jXlV+PVwEWBovIyi9hkyNCyMqV1WY5Nxkco9TSlh8UNhZgNKq2wIC9THDJ6EEOIVImvVOCxc4eB2xZFM2BXAec1tZ5Zxm+xMNhRHomFzLrMqLS8RYQzMKIAZRQhGEcwogpkECEYRQrABYkQAmElEUq9opIS0rWv5zqPFbXq8J1DC4oe6xoVg59EiGv2W+DXf/d3cOK3FqweMMdhyK1Dy3eHGN6hNLBirf5s5ZnpmkgBmcF7Oui2YDWBhtctER+JhEB33fWxip+JKK8J94AcuJSx+ijEGWeEQfewPgxBCfEn03b3BvDhtgRYcPl2Jfp0i1A6DGt36q16JYfgjt1TtMAghLeTfp0z1+Huywn2owS6VsPgpoyTApofW74S0Uo2iYGtJBRTnPDycwzU3j4L6VTCsznXtkPfMOdA9A2DjHHbOYeMcNqX+bdc6520b57ArHFbOIXPuGi6/OdUK/b2S9newoAJd44LVDgMAJSx+LSkyEEcLK9EpKkjtUAhpd5M7RONotQUCHPPyMDjn6AEgMVavxwR3JjS1Ewk6JhnkrkkGJcZgFgSECAwGxiAxBoPztqHO7bOXi4x+wRPfVlZjR9c49duvAJSw+LXYkADsPFpMCQvxSz2DzegZTN37CTmXaquMgHN0hVaD70RCVGEUBVg1PpIjIYQQz/szrww9EkLVDsOFEhY/1yMxFH/mlakdBiGEEB/kS1WWlLD4OVFgUHyoFTghhBD1/ZlbhqQI36oypYSFIDbEhIKyGrXDIIQQ4iOOFlYiNjRA7TDqoYSFoGNEIHJLKWEhhBACnCq3oLsPtV2pRQkLAeBbgwMRQghRz9HCSqRE+17vUUpYCADfalhFCCFEHZxz+OrpgBIW4lJjk9UOgRBCiIr2n6xAt3jfqw4CKGEhThkdwqh7MyGE+LlKqx3BJt8cU5YSFgIAEGjWZkII8WucO6ag8FVtSljmzZsHxhimTZvmWnbvvfciLS0NZrMZMTExGDduHPbu3dvkfjjnePbZZ5GQkACz2Yzhw4fjwIEDbQmNtAKlLIQQ4r+yT1eisw82tq3V6oQlMzMTCxYsQEZGRr3l/fr1w6JFi/DXX39h1apV4JxjxIgRkOVzt4945ZVX8Oabb+K9997Dtm3bEBQUhJEjR6Kmhrratqf4sADkllSrHQYhhBAVlFTbEBFkVDuMc2pVwlJRUYFJkybhgw8+QERERL1199xzD4YOHYqUlBRccMEFmDNnDnJycnDkyJFG98U5x/z58/HMM89g3LhxyMjIwNKlS5Gbm4uvv/66NeGRVkoIM+MkDSBHCCHEB7UqYZk6dSrGjBmD4cOHN7ldZWUlFi1ahNTUVCQlJTW6TXZ2NvLz8+vtKywsDAMHDsSWLVtaEx4hhBBC3PBnbhlSo3y3OggA3G4K/Omnn2LXrl3IzMw85zbvvPMOnnrqKVRWVqJbt25YvXo1jMbGi5ny8/MBAHFxcfWWx8XFudadzWKxwGKxuO6XlVHvFkIIIaQ1CspqYBCZT1cHAW6WsOTk5OCRRx7BJ598goCAc88xMGnSJOzevRsbNmzAeeedhxtvvNGj7VHmzp2LsLAw1+VcpTekdWjUW0II8R8nSqrRNS5E7TCa5VbCsnPnThQUFOCCCy6AJEmQJAkbNmzAm2++CUmSXA1rw8LC0LVrVwwdOhRffPEF9u7dixUrVjS6z/j4eADAyZMn6y0/efKka93ZZsyYgdLSUtclJyfHnadBmhAZZERxlU3tMAghhLSThDAzjhVWqR1Gs9xKWIYNG4Y9e/YgKyvLdenfvz8mTZqErKwsiKLY4DGOft28XhVOXampqYiPj8fatWtdy8rKyrBt2zYMGjSo0ceYTCaEhobWuxDP6BBuxoli6ilECCH+Ij4sAHmlvv+971YblpCQEPTq1avesqCgIERFRaFXr144fPgwPvvsM4wYMQIxMTE4fvw45s2bB7PZjKuuusr1mPT0dMydOxcTJkxwjeMyZ84cdO3aFampqZg5cyYSExMxfvx4jzxJ0nKSKECmKiFCCPErkuj7I3F5dPzdgIAAbNq0CfPnz0dxcTHi4uIwdOhQbN68GbGxsa7t9u3bh9LSUtf92ga699xzD0pKSjBkyBCsXLmyyXYyhBBCCGm78hobzAbfHI6/LsZ10MKyrKwMYWFhKC0tpeohD8jKKUHfpHC1wyCEEOJlnHNsPlSIi7tEq3J8d87fNJcQIYQQ4qe2Zxfh/ORwtcNoEUpYCCGEED+082gR0hNCEWj0/eoggBIWQgghxO8cL65CbEgAwswGtUNpMUpYSANmg4hq67knqySEEKJtJ8tqkBQZqHYYbqGEhTQQG2KiSRAJIUSnOOdgzPe7MZ+NEhbSQJBJQqXVrnYYhBBCvODw6UqkRQerHYbbKGEhDSicQ9Bg9k0IIaR5ZdU2hAVqp+1KLUpYSAMmSYDFrqgdBiGEEOJCCQtpQIt1m4QQQvSNEhZCCCHEj2j1RyklLIQQQgjxeZSwEEIIIX5Em+UrlLAQQgghfkWrMx5TwkIIIYT4CbusQKQ2LERPONdqDk4IIeRcDhRUoEus9gaNAyhhIeeg1VbkhBBCzq3aJsNsFNUOo1UoYSGEEEKIz6OEhRBCCPEDXOPTrlDCQhql3Y80IYSQxhRVWhEZaFQ7jFajhIUQQgjxA6FmA4qrrGqH0WqUsJBGUR8hQgjRF4MowK5od2JbSlhIo6hKiBBC9CcmOAAFZTVqh9EqlLCQRlEJCyGE6E9yVCBySylhITqi0MBxhBCiS1otQaeEhTRKq0M3E0IIaZqs0R+klLCQRmnz40wIIaQpR05XIikiUO0wWoUSFkIIIcRPFFZaEBNiUjuMVqGEhRBCCPEDpdU2hAQY1A6j1ShhIY2SBAarXbv99QkhhNR3+FQFzosLUTuMVqOEhTRKFBgqLHa1wyCEEOIhTOOdKShhIY0ySQJEQdsfbkIIIWfIira7U1DCQhplVzgMIiUshBCiBxa7rPnvdEpYSKMsNgVGkT4ehBCiB3/klqFXYpjaYbQJnZFIo+yKAokSFkII0QXOOQSNV/NLagdACCGE+DvOOX49XgoACAmQkBYT7LF9Hy2sRHJkkMf2pxb6CU0ape2mWYQQoh0VFjs+3noU3RNC0DcpHKXVNo/u/3SFVbODxdVFJSykAbusQNJ40SEhhGhB5pEiBJskXNevI0ySiFPlFsQEey654BqdN6gxVMJCGvgzrwzp8aFqh0EIIbpWWm1DRKAR3RNCEWh0lB/kFFchKdJzc/1UWmWEmfVRNqGPZ0E8yiYrMEqUyxJCiCcVlNXgaFEVBMYgMKCwwoqh58V49ZhGUYBFJ6OWU8JC6jldYUFUkPbrOgkhxJeUVttwvKQaA1Iiz7mNonB4ujLeIDLYZH1UC9HPaFJPTlEVUqK135qcEEJ8yR8nSnFBckST2xw8VYGuHp7rp8amIMCgj1O9Pp4F8RitzzVBCCG+RlF4i6rZKyx2BJs8W/FxsKACnaM910VaTZSwEEIIIV5klRUEBzSfiHjj56JN0U+bRH08C+IxRlFAjU1WOwxCCNGNAIOIvJKaZrsYe7qEW+uTHZ6NGt2SesICDSirsSHAIKodCiGE6MagtCjsPFoMq11B55hgxIcFeP2Y27OL0D+l6XYzWkIlLKSeIKOIKguVsBBCiCcFGET0T4kEGGC1Kyit8uxotnUpCsfmQ6fRPSEEBh3NCaefZ0I8osJiR5CHG30RQghxGNQ5CpsOnsK3v+V6JWmx2hX8fPA0BqREIjzQ6PH9q4nOTKSeU+UW9E0KVzsMQgjRJcYY0uND0a+Td6pqMo8UYUiXaM3PzNwYSlhIPQqnrs2EEKI1nHNsPVyEPknhukxWAEpYyFkoVyGEEM/ZfawYgOPHoMAADiD7dKXHS1i2HC5E36Rw15xEeqTfZ0bcVlxpRaTO6jwJIUQtn2w7ii4xwRjYOare8o4R5ka3b8nvRatdQbVVRligwbUs+3QlusWF6DpZAShhIXUcL65G9wTPDgtNCCH+qMYmI6NDOHp3DGuwLjbE/S7NnHNszy5CkEmC2Shiz4lSZCSFIb+0BqcrLEiNjvZE2D6NEhbiInMOSUdd4AghRC3r9hZgVK94j+zLJivYergQF6ZGwiQ5xsjqHB2EgwUV6BBuxnkenn/IV1HCQgghhHjQzqPFGHpejMc6MOw4UoyL0+r3/GGMeXyiRF/Xpp/T8+bNA2MM06ZNAwAUFRXhoYceQrdu3WA2m5GcnIyHH34YpaWlTe7njjvuAGOs3mXUqFFtCY0QQghpd6fKLQgzG1o1nlVjA+kfOlWBtJgg3fb8cUerS1gyMzOxYMECZGRkuJbl5uYiNzcXr776Knr06IGjR4/ivvvuQ25uLr744osm9zdq1CgsWrTIdd9kMrU2NNJKzc1zQQghpGmHTlVgYGqkR/ZVWmVDlUVGWow+Zltuq1YlLBUVFZg0aRI++OADzJkzx7W8V69e+PLLL13309LS8OKLL+LWW2+F3W6HJJ37cCaTCfHxnqnvI61D468QQkjrcc5hlASPfJcqCsfvuaW4uIv+G9O2VKuqhKZOnYoxY8Zg+PDhzW5bWlqK0NDQJpMVAFi/fj1iY2PRrVs33H///SgsLDznthaLBWVlZfUupG1qbDKM1OCWEEJa7WhhFTpFBrb68XXTnO1HijAgxTMlNXrhdgnLp59+il27diEzM7PZbU+fPo0XXngB99xzT5PbjRo1Ctdeey1SU1Nx6NAh/P3vf8fo0aOxZcsWiGLDWYPnzp2L2bNnuxs6acKRwkqkRLf+D40QQvxdSbUNnaJa/z1aWym/L78cnWOCYJToR2RdbiUsOTk5eOSRR7B69WoEBDTdj7ysrAxjxoxBjx49MGvWrCa3vfnmm123e/fujYyMDKSlpWH9+vUYNmxYg+1nzJiBxx57rN6xkpKS3Hkq5CzVVln3gw4RQog3/bgnD4lhAYgNdX+clVoF5TUAWjdWi965lb7t3LkTBQUFuOCCCyBJEiRJwoYNG/Dmm29CkiTIsgwAKC8vx6hRoxASEoIVK1bAYDA0s+f6OnfujOjoaBw8eLDR9SaTCaGhofUuhBBCiJp6dQhDVHDrO4zYZAXZpyrRLd6/uiu3lFs/qYcNG4Y9e/bUW3bnnXciPT0dTz/9NERRRFlZGUaOHAmTyYRvv/222ZKYxhw/fhyFhYVISEhw+7GkdajBLSGEtA1jQFGlFTEhrUtaYoJNSG5DGxi9c6uEJSQkBL169ap3CQoKQlRUFHr16oWysjKMGDEClZWVWLhwIcrKypCfn4/8/HxX6QsApKenY8WKFQAcPY6efPJJbN26FUeOHMHatWsxbtw4dOnSBSNHjvTssyWEEEK8JDrYhCc+/7XVj0+JpvFWmuLRRgu7du3Ctm3bAABdunSpty47OxspKSkAgH379rkGkxNFEb/99huWLFmCkpISJCYmYsSIEXjhhRdoLJZ2UlplQ0gAtV8hhJC2uKhzFB74ZBdssgID9br0OMZ1MFpYWVkZwsLCXF2oiXt2HytG36RwqhYihJA2yimqwtbDhY0Om89wpidQ7bdttU1G36RwBBga9oj1B+6cv+lnNQEHtWEhhBBPSIoMRFIL26EoCsfmQ4V+m6y4i8qs/BwNGEcIIerYml2IgZ1pcLiWojOVn/sjtww9EqgajRBC2tPvJ0qRHh9KbV3cQFVCfk5YsxIlRtnRH48BcM6WDVcVEWu4DnCur7Nd7TbAmUpazh0XOK65ojjWcaX+fUWpsw0HFAVclgFFBrc3vOaKDMYYOOf1qrJM3dIRfu0Er75ehBDSVjlFVQgySYgMMqodiqZQwuLHDv64FtHcguBLr3AlF6422Nz5D+fnWFdnPXBmu3oJDVyJDROcyY0gNLzfyDZMFAFRdFwLIpgkggkCIIqO9Y20uTk5d543XiZCCPGYshobCiut6JsUrnYomkMJi5+qzjmOyv/+F2mL33ckAoQQQrxKVjj2HKcZmFuLzlR+qvC3P8GjYyhZIYSQdrItuxADU6mRbWvR2cpPScFBCBk8SO0wCCHEL/x+ohTd40MhUSPbVqNXzk8V/LQena68VO0wCCFE944VViHYJCGCGtm2CSUsforV1EAKC1M7DEII0S3OOfafLEdxlRUp0UFqh6N51OjWD1lPFoBHRqkdBiGE6E6lxY79J8tdnSm7xAYjNMCgakx6QQmLHzq0ZiM6DB2sdhiEEKILxwqrUFhpAQcQaBTRp2M4zbrsBZSw+CFb1m5E3jRe7TAIIUSTLHYZ+/LLISscHEByZCCSoyLUDkv3KGHxM1Un8sDCI8AkeusJIcQdssKRlVMMgyigewINq9/e6KzlZw4sXoZukyeqHYbH8bqj7BJCiIdwznH4dCVKq20AgD4dw6lrskooYfEjssUCFBchoGMHtUPxPFl2DvtPCCGt4+jVU4EKix1inTYoqVFBSIsJVjEyAlDC4lf2f/YNOowfq3YY3qEoYPSrhxDSClVWO/7KKwMAnBcXghDq1eOT6Bvej9h270LUxRepHYZXKFYbKjdvUTsMQojGHCwox8GCClyQHIF+nSIpWfFhVMLiJ47t3IOQ89IaneXYkwoLCzFs2DDX/aqqKhw+fBgFBQU4dOgQpk2bhoqKCjDG8K9//QtXXHGFR47LJBGBAwd6ZF+EEP/w+4lSRAeb0CU2RO1QSAtQwuInSj7/HD2fecrrx4mKikJWVpbr/quvvooNGzYgIiICEyZMwOLFizF8+HDs378fw4cPx759+2A2m9t+YMao5xMhpMVOlFQj0CgiPixA7VBIC1GVkB8oPnQEhvBwiMHt32hs4cKFmDJlCgoLC3Hq1CkMHz4cAHDeeechPDwcP/74o+cOxnnz2xBC/F6NTUZuSTU6U0NaTaGExQ/kLFqKLvfc0e7H3bx5M4qLizF27FhER0cjISEBy5cvBwBkZmZi3759OHLkiGcORl2aCSEt9PuJUvTvRAO9aQ0lLDpnyT8JGAwwREa2+7EXLlyI22+/HZKzquabb77Bhx9+iPPPPx9vvPEGhgwZ4lpHCCHtgXMOxuD19nzE8+hsoXMHFixCl7vvbPfjVlRUYPny5cjMzHQt69OnD1auXOm63717d/Ts2dODR6UqIUJI0/bmlyM9PlTtMEgrUAmLjlkLiyBbrQhMjG/3Y3/22Wfo06cP0tPTXcvy8vJctz/44AMEBQV5rpeQR/ZCCNG7SosdQSb6ra5F9K7pFefY9+930fWuGwFLOcAVR6PU2mvwM41UGQPAzrQDMQYBkqlNh1+4cCHuvvvuesvef/99fPLJJ+Cco3v37lixYgUVyxJC2s3pCguigtv23UbUwzjXfteKsrIyhIWFobS0FKGhVNQHAJYvHsOxF79DaHJ14xuc/a6zM8vNKaEIffMPb4bncdxqxak330TsE0+oHQohxEdlHinCgJT2b89Hzs2d8zeVsOhRTiZMvy9Elxf+BtZpkKPkhAlwlKII9e/XZi61pS4H/g8nP/wfNJf2UUkNIaQZdecHItpDCYve2K2wfvMwENMbxqteAQTRvceX5wP4n1dCI4QQtVjsMkwSNdvUMkpYdEZZPw9S4X4I96xzP1mppdFaQh3UbhJCvORUuQWxITSqrZZRuqknx3eC/fI6cOlTQEKG2tG0L1EEZEXtKAghPqq40obIIKPaYZA2oBIWvbBVw/rlPUBsbxgveaz1+9FoWxAmCJotGSKEeJ/CObVh0ThKWHTCvvIfkEqPQbhvEyDS9OiEEEL0hRIWPdj7PaSdC8Gveg2ITW9++ybV6TlECCGE+Ahqw6J1xUdhXzEVti6jwQZMUTsaQgjxSfQzTPsoYdEyaxWsy24BN4XAcO07nml/otE2LIQQQvSNqoS0inPYvroPYvEhiH9bAwR6avRGqhIihOgPDXugfVTColF86zsw7P0G4rULgPheaodDCCE+zSAKsNHQB5pGCYsWHdsG/n/Pwj7wfqDHOM/um6qECCE61CkqEEcLq9QOg7QBJSxaU5YL+2e3wZ5wAaQRL6gdDSGEaEJIgAEVFrvaYZA2oIRFS6xVsH5yMzgEGCd+5KXxVqiEhRCiTwq1Y9E0anSrFbIdti/+Bun0PghTVgEh8d45Tm2VEOcarB6iLyNCyLkJjEFWaMRbraKERQsUBdZPb4Ph4Eqwm/8LJPb1/jE1mbBoLV5CSHvinNO3hIZRlZAW2GtgPPADWL87gG6jvHwwRgUVhBDdEqh0RbMoYdGCY5sd131uaceDUtZCCNEPReH0raZxlLBoQOWmd2CP7QV07O/9g2muGogQQpp3oKAC3eJC1A6DtAElLL6uJAeBR3+CNPCedkom6jS6JYQQnaiy2hFkomabWkYJi4+zb1sAxRDk+QHimkUJCyFEP+gbTfso3fRlVUVgmQvBLrofMIe3zzEZw4lKE/567UlAkMAEARAEgDEwQThzYQKYIAICAxNE5/3aS+1yAYIgAoxBEEQwsc52ouhYz0TXY+peC4LovO14rFAnljPbMTAmgokCBCbBVkOjWBJCGqqxyTCK9Ptc6yhh8WF867sAOIRBD7Trcb8bJWPimOugMAFckcEVBUrda1kG54rjWpHBOXeuP7ONoiiAokCRZXAuA4odnFvAZQVcUc7sQ1Gcj+MAV1z7g3xmHTiHIssAOKBwQFHAueK47bzmioKjERV4sF1fKUKIFuw/WY4eCaFqh0HaiBIWX1VyDGzjK+AX3gcERbfjgRkCTAq6njcQkIzteNy2e3n7y2qHQAjxQbLCIVEJi+bRO+ij+DuDAQBS/zvb98Cuhr3aq/Fl1MOJEEJ0ixIWX1SeDy7bwM8bBcSmqxMD9RIihOhAYYUFkUHaKi0mjaOExQfZ178CRTSBTVigwtGplIIQoh9BJgnlNTRLsx60KWGZN28eGGOYNm0aAKCoqAgPPfQQunXrBrPZjOTkZDz88MMoLS1tcj+cczz77LNISEiA2WzG8OHDceDAgbaEpl1F2RB2LYE49LEGPYM2btyIq6++GomJiWCM4euvv6633rOvI5WwEEK0bceRIuzLL4es0PeZHrQ6YcnMzMSCBQuQkZHhWpabm4vc3Fy8+uqr+P3337F48WKsXLkSU6ZMaXJfr7zyCt58802899572LZtG4KCgjBy5EjU1NS0NjzNsm/4J+SACLAL72mwrrKyEn369MHbb7/d6GM98joyGjiOEKIPkiigT1I4+iSFqx0K8QTeCuXl5bxr16589erV/NJLL+WPPPLIObddvnw5NxqN3GazNbpeURQeHx/P//nPf7qWlZSUcJPJxP/73/+2KJ7S0lIOgJeWlrr1PHzO6YNcnhXJlV/ebHZTAHzFihWu+554HTnnnP+6nL/8RjLnlkp3IvcJL29/We0QCCE+wmaXedaxYrXDIM1w5/zdqhKWqVOnYsyYMRg+fHiz25aWliI0NBSS1HgP6uzsbOTn59fbV1hYGAYOHIgtW7Y0+hiLxYKysrJ6Fz1QdiyCYgwBG3C3249tzevYNO2VsHAqFSKEOGUeKUavDmFqh0E8yO1xWD799FPs2rULmZmZzW57+vRpvPDCC7jnnobVG7Xy8/MBAHFxcfWWx8XFudadbe7cuZg9e7YbUWuAIkP+9TMIva8HDAFuP7w1r2OjNNw1mLo1E+LfckuqcaSwEkFGCT0SQiEK9J2gJ24lLDk5OXjkkUewevVqBAQ0fVItKyvDmDFj0KNHD8yaNastMTYwY8YMPPbYY/WOlZSU5NFjtLvD62CoKgDOn6huHFRKQQjRkJIqKw6dqgDnQGxIAAantedAm6Q9uZWw7Ny5EwUFBbjgggtcy2RZxsaNG/HWW2/BYrFAFEWUl5dj1KhRCAkJwYoVK2AwGM65z/j4eADAyZMnkZCQ4Fp+8uRJ9O3bt9HHmEwmmEwmd0L3ebZdy6CEd4Ep8YLmN25Ea17HRnHFcc3EVsVBCCHtJaeoCiVVNvTrFKl2KKQduNWGZdiwYdizZw+ysrJcl/79+2PSpEnIysqCKIooKyvDiBEjYDQa8e233zZbEpOamor4+HisXbvWtaysrAzbtm3DoEGDWvestMZaCXHfdzD2m9TqKhmPvY5cdlyztg/R8/DDDyMlJQWMMWRlZbmWjxgxAhkZGejbty8uueQS7N69u83HIoT4l4LyGpTX2NG7I7VT8RdulbCEhISgV69e9ZYFBQUhKioKvXr1ciUrVVVV+Pjjj+s1iI2JiYEoOn61p6enY+7cuZgwYYJrHJc5c+aga9euSE1NxcyZM5GYmIjx48d75ln6uoNrIcgWoPs1TW5WUVGBgwcPuu5nZ2cjKysLkZGRSE5O9szrqDgTFqHtJSzXX389nnrqKQwZMqTe8uXLlyM8PBwAsGLFCtxxxx349ddf23w8Qoj/OFZYhf4pVLLiTzw6+eGuXbuwbds2AECXLl3qrcvOzkZKSgoAYN++ffUGk3vqqadQWVmJe+65ByUlJRgyZAhWrlzZbOmMXlh3LQOPyYApukuT2+3YsQOXX365635tO57Jkydj8eLFnnkdueLoH+SBEpahQ4c2urw2WQEcvcg82ViWc06NbwnxA9Sg1v+0OWFZv3696/Zll13Woq6lZ2/DGMPzzz+P559/vq3haE/FKRgOrQYb9VKzmzb3+nrkdeQyGJjXewvdfvvtWLduHQDghx9+8Mg+Nx3fhEf7PQoDO3ebKUKIPthk6iDgbzxawkLcx3ctBWcCWO8b1A7FgSvg7VBCsXTpUgDAkiVL8PTTT3skabk86XJHskUI0Y3SahsOnCyHJAqQFQWBRglhZgNCAuj05W9o8kM1WSogb34LSp9bgEAfqYtVFLTnBIiTJ0/GunXrUFhY2PadMRo8jhA9KSirwZHTleifEom+SeHo1ykSEYFGrN93Ct0TQtUOj7QzSlhUxLe/D8FaDunSx9UO5Qwue7UNSElJCXJzc133v/76a0RFRSEysu0JGwMD1+AIvYSQxh0prGowD1B8WACu6ZuoTkBEVVSmppaSY1A2vgZ+/mQI4clqR+OiKLLHqlXuvfdefP/998jPz8fIkSMREhKCtWvX4oYbbkB1dTUEQUBMTAy+++47jyRJlLAQoh+yws/ZsDbYRKcuf0TvuhoUBdYv7wczhcJw5bNqR1OPrNgheqiEZcGCBY0u3759u0f2fzbGGFUJEaITu44Voy/NskzqoIRFBXz7+zDm/Azc/g0Q4FuDHimKDYJGG65SCQsh+mEQBRhEarVAzqBPQ3s7+Qf46udg73830PkytaNpQOayZhMWgBrdEqIX9LdMzkYlLO2p+ChsH10PHtkFxhG+OeaMosgeqxJqbzRgHCH6UFBeg6ggfc0XR9qOSljaS0UBbIvHA4IBxtu+AIyBakfUKFmxa7aEhaqECNE+zjkOnqxAcpRvfkcS9VAJS3soz4dt4WgwWyWkv60CQhOaf4xKZC5D9MCw/GqgRreEaNOxwirkl9XAIDIoHOibHK52SMQHUcLibUWHYVs8DpCtkO76AYhMVTuiJimKHSKVsBBC2kl5jQ1lNTZcmOojg2cSn6XNn9JacWo/7B9cCQgSDHevBpqZ3NAXyFymhIUQ0m7+yC1Drw6+1VuS+CYqYfEWSzlsy24BAsJh+NsqICha7YhaRFFkCFptvEpD8xOiKUdOVyIlKkjtMIhGUMLiDZWFwD87wwAAUzM1k6wAtQPHabPgjSY+JERbCiut6NcpQu0wiEZo88zky07shP2dQY7b1y0EYs5TNx43yVyBQB8LQoiXVVrsCDSKaodBNITOTJ505BfIi6+BEtoReOwvoPf1akfkNi23YVG4AkGjpUOE+BPOOTYdOIX0+BC1QyEaQlVCnsA5eOZ/wFf+HXLHgTBO+hQwBasdVasosg2CoM1fPXbFDpFpM3ZC/AHnHNuziyCJDJd1i6XBHolbKGFpq7JcWL+ZBuOhVZD73w3jqBcBSbsjNMqKTbMnfYUrEDWabBHiD34/UYaMjuEwU1UQaQVKWNriyC+wfzoJgmAAbl4GKX2M2hG1maLYNVvCQlVChPg2u6JQskJajRKW1lAUKD+/DrZuDpQOF8F4yydAoD4GPVIUGwSmvY8F5xyFNYWQNBg7If6CBh0gbUHf7u46vgPW76fDkLcDfMjjMF4+AxD18zLKih2SBktYtuRtwYD4AVQnTogP0+wYT8Qn6OdM622cg//yJrDmOSCqO9itX4J1GaZ2VB4nK3YIgnY+FpxzLP1zKYprivHg+Q+qHQ4hpAkKDexI2kA7ZyY1lR6HdcVDMB75CfYB98A4eh6gwVKIlpAVu6Yarh4vP45KWyWm9ZumdiiEkGYESCKqrHYEGunUQ9xHn5pm8D1fgH05BUYAiO8NyRwGrJ8HMAYwAQBz3AaDY/iSuveFOrfPvq5dV3ug2l8evIn7Z90+5zWaWX/ufSklR1ESmYTcilwwMFcVS+3tRq/rbnfWstrRZxt7jOP/hvt0x5a8LYgM0Ef7IUL0Lj0+BLtzSmh0W9IqlLA0w3o8CyYAdmMolIpiYOcyOE72jpM8g3ImuXCe9Fm95EBxnLJdScKZdYwrQJ1B2ni9k3Wd267ldZOe2pSjfjLEz9qm4bK626LBsmQmYFtAIpb8+j/XERRnzLUTC3LXbef6Os+Nn7W+9r7jtvPamVDV/uf4/8z+m8brvTYWuQY9I/rh15ySOq+J72JoXYx1H9fS241p7eNa4nSFBcO6x7VhD0TvBKG1fwGEAIzrYLa4srIyhIWFobS0FKGhoWqHQ4hf+jWnBH2SwtUOg/i4/NIayJyjQ7hZ7VCID3Dn/E2DVhBCCGk38WEBOFlWo3YYRIOoSogQQkircM5hsSuw2BTU2GXU2GTU2BRY7DKaKruvscntFyTRDUpYCCFEh2SFOxMIGdXORKLGJsOucI9Mb1qbj5gkAQEGEQEGAaEBBsSGiDBJgrO9CiGeQwkLIYS0A845amyKM3k4UxphlZUmH9OawRAZHIO0BRhqkwkR4YFGBEgCJJFaAhBtooSFEOKXOOewKxxWu+Ko1rDLsNjO3FbOqtI4u39L3TSisdoP1si6AIMAszOBiAoywWQQYJIEGqGZkBaghIUQ0q4457DKCqx2xZUsWO2Ka5lNVtw+gbemJIJzDoPoSBhMkgijJCDMbIDRWcUhUpUGIT6FEhZCiEeYDAKynOPh1NVYMmEQGUySAKMowmQ4kyiYqMqCEHIOlLAQQjwiPZ7GQCKEeA/9lCGEEEKIz6OEhRBCCCE+jxIWQgghhPg8SlgIIYQQ4vMoYSGEEEKIz6OEhRBCCCE+jxIWQgghhPg8SlgIIYQQ4vMoYSGEEEKIz6OEhRBCCCE+jxIWQgghhPg8SlgIIYQQ4vMoYSGEEEKIz6OEhRBCCCE+T1I7AE/gnAMAysrKVI6EEEIIIS1Ve96uPY83RRcJS3l5OQAgKSlJ5UgIIYQQ4q7y8nKEhYU1uQ3jLUlrfJyiKMjNzUVISAgYYwAcWVtSUhJycnIQGhqqcoQEoPfE19D74XvoPfEt9H54H+cc5eXlSExMhCA03UpFFyUsgiCgY8eOja4LDQ2lD5qPoffEt9D74XvoPfEt9H54V3MlK7Wo0S0hhBBCfB4lLIQQQgjxebpNWEwmE5577jmYTCa1QyFO9J74Fno/fA+9J76F3g/footGt4QQQgjRN92WsBBCCCFEPyhhIYQQQojPo4SFEEIIIT6PEhZCCCGE+DxdJiz79+/HuHHjEB0djdDQUAwZMgTr1q1zrV+8eDEYY41eCgoKVIxcv5p7T2otXrwYGRkZCAgIQGxsLKZOnapCtPrXkvejsb+PTz/9VKWI9a2lfx8AUFhYiI4dO4IxhpKSkvYN1I80954UFhZi1KhRSExMhMlkQlJSEh588EGa086LdJmwjB07Fna7HT/99BN27tyJPn36YOzYscjPzwcA3HTTTcjLy6t3GTlyJC699FLExsaqHL0+NfeeAMC//vUv/OMf/8D06dPxxx9/YM2aNRg5cqSKUetXS94PAFi0aFG9v5Px48erE7DOtfT9AIApU6YgIyNDhSj9S3PviSAIGDduHL799lvs378fixcvxpo1a3DfffepHLmOcZ05deoUB8A3btzoWlZWVsYB8NWrVzf6mIKCAm4wGPjSpUvbK0y/0pL3pKioiJvNZr5mzRq1wvQbLf0bAcBXrFihQoT+xZ3vrHfeeYdfeumlfO3atRwALy4ubudo/UNrziOcc/7GG2/wjh07tkeIfkl3JSxRUVHo1q0bli5disrKStjtdixYsACxsbHo169fo49ZunQpAgMDcf3117dztP6hJe/J6tWroSgKTpw4ge7du6Njx4648cYbkZOTo3L0+uPO38jUqVMRHR2NCy+8EB9++GGLpoAn7mnp+/Hnn3/i+eefx9KlS5udJI60TWvOI7m5ufjqq69w6aWXtnO0fkTtjMkbcnJyeL9+/ThjjIuiyBMSEviuXbvOuX337t35/fff344R+p/m3pO5c+dyg8HAu3XrxleuXMm3bNnChw0bxrt168YtFouKketTS/5Gnn/+ef7zzz/zXbt28Xnz5nGTycTfeOMNlSLWt+bej5qaGp6RkcE/+ugjzjnn69atoxIWL2vpeeTmm2/mZrOZA+BXX301r66uViFa/6CZhOXpp5/mAJq8/PXXX1xRFH7NNdfw0aNH859//pnv3LmT33///bxDhw48Nze3wX43b97MAfAdO3ao8Ky0zZPvyYsvvsgB8FWrVrn2X1BQwAVB4CtXrlTrKWqKt/5Gas2cOZOKu93gyffj0Ucf5TfddJNr35SwtI43/kby8vL4X3/9xb/55hveo0cP+vHrRZoZmv/UqVMoLCxscpvOnTtj06ZNGDFiBIqLi+tNB961a1dMmTIF06dPr/eYKVOmYNeuXdi9e7dX4tYzT74nixYtwl133YWcnBx07NjRtU1cXBzmzJmDu+++22vPQy+89TdS6/vvv8fYsWNRU1NDc6u0gCffj759+2LPnj1gjAEAOOdQFAWiKOIf//gHZs+e7dXnohfe/hv5+eefcckllyA3NxcJCQkejZ0AktoBtFRMTAxiYmKa3a6qqgoAGtTxCoIARVHqLauoqMDy5csxd+5czwXqRzz5nlx88cUAgH379rkSlqKiIpw+fRqdOnXyZNi65Y2/kbqysrIQERFByUoLefL9+PLLL1FdXe1al5mZibvuugubNm1CWlqaB6PWN2//jdSus1gsbYiSnJPKJTwed+rUKR4VFcWvvfZanpWVxfft28efeOIJbjAYeFZWVr1t//Of//CAgAAqVvWylr4n48aN4z179uS//PIL37NnDx87dizv0aMHt1qtKkavPy15P7799lv+wQcf8D179vADBw7wd955hwcGBvJnn31W5ej1x53vrFpUJeRdLXlPvv/+e/7hhx/yPXv28OzsbP7dd9/x7t2784svvljl6PVLdwkL55xnZmbyESNG8MjISB4SEsIvuugi/sMPPzTYbtCgQfyWW25RIUL/05L3pLS0lN911108PDycR0ZG8gkTJvBjx46pFLG+Nfd+/Pjjj7xv3748ODiYBwUF8T59+vD33nuPy7KsYtT61dLvrFqUsHhfc+/JTz/9xAcNGsTDwsJ4QEAA79q1K3/66afpPfEizbRhIYQQQoj/os78hBBCCPF5lLAQQgghxOdRwkIIIYQQn0cJCyGEEEJ8HiUshBBCCPF5lLAQQgghxOdRwkIIIYQQn0cJCyGEEEJ8HiUshBBCCPF5lLAQQgghxOdRwkIIIYQQn0cJCyGEEEJ83v8DH+RqGLbvCVwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 774870.5 193717\n",
      "You may suggest [[10], [34, 5, 64], [50,82,52,42,63], [31,78,75], [73] ] \n",
      "let's decide whether to delete, accept, or modify list [10] with pop 154316.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's decide whether to delete, accept, or modify list [34, 5, 64, 8, 25, 31, 71, 56] with pop 191283.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [34, 5, 64]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [34, 5, 64, 8, 25, 31, 71, 56] with [34, 5, 64]\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if you want to manually add another corner-county cluster 1\n",
      "Type in a [county list], where the corner county is first in the list.  Or type 0 to stop [50,82,52,42,63]\n",
      "enter 1 if you want to manually add another corner-county cluster 1\n",
      "Type in a [county list], where the corner county is first in the list.  Or type 0 to stop [31,78,75]\n",
      "enter 1 if you want to manually add another corner-county cluster 1\n",
      "Type in a [county list], where the corner county is first in the list.  Or type 0 to stop [73]\n",
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "154316.0 [10]\n",
      "24060.0 [34, 5, 64]\n",
      "88252.0 [50, 82, 52, 42, 63]\n",
      "125341.0 [31, 78, 75]\n",
      "160383.0 [73]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copied 2/17/24 from CO\n",
    "\n",
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in has1neighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in hasFewNeighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "print(\"You may suggest [[10], [34, 5, 64], [50,82,52,42,63], [31,78,75], [73] ] \")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15497\n",
      "We defined a total of 2 unit (but not corner-cluster) counties in the map, excluding the cut-out districts\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in uncutCountyList:\n",
    "    if countyGeom[c].intersects(MAPexterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop and c not in allFusedCounties:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map, excluding the cut-out districts\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "232 fragmented VTDs out of 4805\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 232 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 10\n",
      "looking for surrounds, now working on vtd 605\n",
      "looking for surrounds, now working on vtd 1174\n",
      "looking for surrounds, now working on vtd 1691\n",
      "looking for surrounds, now working on vtd 2231\n",
      "looking for surrounds, now working on vtd 2759\n",
      "looking for surrounds, now working on vtd 3353\n",
      "looking for surrounds, now working on vtd 3864\n",
      "looking for surrounds, now working on vtd 4551\n",
      "WARNING.  We did not find another neighbor of surrounder 493 other than surroundee 4868\n",
      "On loop 2 for county 72  we found that vtd frag 278 now has only 1 neighbor 4943\n",
      "special for MI, I believe that [3647, 5145] are surrounded by vtd (fragment) 3646 . Here, let me show you\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the surrounding operation 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After surround checks, we appear to have 4148 building blocks defined. We captured 231 surrounded VTDs\n",
      "WARNING - unit 3340  = vtd 3683 in county 2 has only 1 neighbor= [3302] . Optional triage in next block\n",
      "working on unit neighbor lists for county 40\n",
      "working on unit neighbor lists for county 60\n",
      "working on unit neighbor lists for county 80\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[cc])\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                raise Exception(\"neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "#NEW for MI -- allow manual surrounding entry\n",
    "specialSurrounded = [3647, 5145 ]\n",
    "specialSurrounder =  3646\n",
    "print(\"special for MI, I believe that\", specialSurrounded,\"are surrounded by vtd (fragment)\",specialSurrounder,\". Here, let me show you\")\n",
    "c = countyNo[parentVTDno[specialSurrounder]]\n",
    "plotPoly(countyGeom[c])\n",
    "for v in specialSurrounded:\n",
    "    plotPoly(fragVTDgeom[v])\n",
    "    plotCenter(v,fragVTDgeom[v])\n",
    "plotPoly(fragVTDgeom[specialSurrounder],0.2)\n",
    "plotCenter(specialSurrounder, fragVTDgeom[specialSurrounder], 8)\n",
    "plt.show()\n",
    "shouldIcombine = int(input(\"enter 1 to apply the surrounding operation\"))\n",
    "if shouldIcombine == 1:\n",
    "    for v in specialSurrounded:\n",
    "        vv = specialSurrounder\n",
    "        if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "            for sNo, s in enumerate(surroundedVTDs):\n",
    "                if surrounders[sNo] == v:\n",
    "                    surrounders[sNo] = vv\n",
    "        surroundedVTDs.append(v)\n",
    "        surrounders.append(vv)\n",
    "        nVTDneighbors[vv] -=1\n",
    "        nVTDneighbors[v] = 0 #we are capturing all surroundees (some of whom may touch other surroundees)\n",
    "        fragVTDpop[vv] += fragVTDpop[v]\n",
    "        fragVTDpop[v] = 0\n",
    "\n",
    "\n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. unsurrounded vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, the piece we selected is surrounded.  KISS - take on this neighbor's neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "            unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e79873ab-0b8e-48a9-b4b0-05b3135b9cc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should always be the case\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "    \n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 106\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ef3e1dd5-245a-4f64-88ab-25af0c9cfb71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3443 1370 862.5320544533315\n",
      "u, v are 3303 3647\n",
      "u, v are 3342 5145\n",
      "u, v are 3302 3646\n"
     ]
    }
   ],
   "source": [
    "print(\"ignore this block, we used it to find the manual border surround in MI\")\n",
    "print(unitPop[3302], unitPop[3303], unitPop[3342])\n",
    "for u in [3303, 3342, 3302]:\n",
    "    print(\"u, v are\",u, allUnits[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/MI4805convexHD2_12nW4_21Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 1 - we already have an ensemble of HDpoly's and their weights (often from running option 2 first)\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"pigs\" == \"can fly\": #countyNo in shapeDF.columns.values:\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "if len(unassignedList) > 0:\n",
    "    if len(unassignedList) > 20:\n",
    "        print(\"  (too many to plot individually)\")\n",
    "    else:\n",
    "        print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "        for t in unassignedList:\n",
    "            print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "            plotPoly(hdCP[t].buffer(0.1))\n",
    "            plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "            plotPoly(countyGeom[HDcountyNo[t]])\n",
    "            plotPoly(MAP,0.2)\n",
    "            plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "3f0f8434-5f7a-41d1-bd86-07ec230b4aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, let me renormalize the HDweights if there were cut districts\n",
      "our HDweight sum was 0.9227091975045879 but is now 0.9999999999999998\n"
     ]
    }
   ],
   "source": [
    "print(\"now, let me renormalize the HDweights if there were cut districts\")\n",
    "sumWt = np.sum(HDweight)\n",
    "HDweight = [HDweight[t]/sumWt for t in range(nHDs) ]\n",
    "\n",
    "print(\"our HDweight sum was\",sumWt,\"but is now\",np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2fb6d6e3-6157-4728-a6e5-ea22ede3de03",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPTIONAL - write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"04Apr24\"\n",
    "print(\"OPTIONAL - write the unit topology to a file\")   #New for FL 2/17/24 - include parent VTDs\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "c47fa1ff-2bed-4641-96e9-8dbdb7cb6da9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use fraction for corner clusters is 1.00, but can enter higher number to account for later discontig rejects\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter maxCCBfrac (e.g. 1.0) prior to discontig shedding 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cluster no 0 containing counties [10]\n",
      "halfway there! last use was 1.0\n",
      "our final fractional capture area to normalize this cluster's usage is 0.40622 from 326 intersecting HDs\n",
      "working on cluster no 1 containing counties [34, 5, 64]\n",
      "halfway there! last use was 0.999\n",
      "our final fractional capture area to normalize this cluster's usage is 0.19877 from 447 intersecting HDs\n",
      "working on cluster no 2 containing counties [50, 82, 52, 42, 63]\n",
      "halfway there! last use was 0.96835\n",
      "our final fractional capture area to normalize this cluster's usage is 0.20105 from 413 intersecting HDs\n",
      "working on cluster no 3 containing counties [31, 78, 75]\n",
      "halfway there! last use was 0.84137\n",
      "our final fractional capture area to normalize this cluster's usage is 0.46637 from 411 intersecting HDs\n",
      "working on cluster no 4 containing counties [73]\n",
      "halfway there! last use was 0.84421\n",
      "our final fractional capture area to normalize this cluster's usage is 0.57622 from 356 intersecting HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing w/option 1 - set the cluster area thresholds to unitize cluster use\n",
    "#NOTE -- THIS MAY NOT BE RIGHT -- DEPENDS ON HOW WE GENERATED THE HDpoly's\n",
    "print(\"default use fraction for corner clusters is 1.00, but can enter higher number to account for later discontig rejects\")\n",
    "maxCCBfrac = float(input(\"enter maxCCBfrac (e.g. 1.0) prior to discontig shedding\"))\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    nIntersections = 0\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < maxCCBfrac - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #maxCCBfrac vs. 1.0\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t].intersection(MAP.buffer(1)),0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]),\"from\",nIntersections,\"intersecting HDs\" )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t].intersection(MAP.buffer(1)),1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aad5e7db-93db-43d8-8196-dd6dd02f3014",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\n"
     ]
    }
   ],
   "source": [
    "#print(\"I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\")\n",
    "#CCBareaFrac = [0.17, 0.25, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "217527a2-0acf-43ec-9a25-09e0aecce76d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\n",
      "We'll later add nearby or jettison faraway contiguous units until we approach the aDP\n",
      "  the HD pops and lists already appropriately include captured corner county clusters\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HDno 10 time in sec is now 0\n",
      "working on HDno 607 time in sec is now 6218\n",
      "working on HDno 1176 time in sec is now 9535\n",
      "working on HDno 1696 time in sec is now 14724\n",
      "working on HDno 2238 time in sec is now 20650\n",
      "working on HDno 2788 time in sec is now 20663\n",
      "working on HDno 3365 time in sec is now 21372\n",
      "working on HDno 3877 time in sec is now 21385\n",
      "working on HDno 4567 time in sec is now 37541\n",
      "all done assigning units to HDs based on original HD polygons\n",
      "Here is a scatterplot of active HD pop excess vs relative to target 774870.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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exwc7f8OV2gaUX63Hki3Hse6ns7Lcd3fGLRbELMBceSnFvuu+si1HIRryQcZWM8FUcdXtY7Bsw3lj0vW2Z8VXBk6l8YZXTa2InaRYgr89+Syx3svF2Ufw3o6iJn9fLNMWk5Jb/2JEfeX0UvrSthyFaMgHGVvNBOvBWyqsq0BvTLrDE42Ymn4zPt5VhPKrN1LTlfas+NLA6Qm84VVTI2ImKaHgb289S0L3cvPB8w4NLR4OTb13Yj3ASm79s45Gcm6l+dq2HIVoyAcZW82EqBZ6yX8rZRvOk5Ouo8kq4nrsiBLZdzwsA+f8DYfRMjgIF6/UNmtPT3ODdfI5dbEab2351WVMkhon4QYzh5e+LhBsx3vv+iVEYdm2E00WQ0JeO7m0rxxRVl3PtP0l51aar23LqSFEw1+gcj3NBGO4tJWH2gOcNx8sxiQHpUUqrtbjrS3HkVNoUuzcLAOnqbIWYz/Yi2fX5WPMqj0YuGgbla5pBvCTlLM3RgPAGK7H2n1nmIwI+1Iw3i5Lta+olFncNKfQhJRXc7Bky682hhYgXM7JVdkqOWDxQMq5leZr23Jiy4YRzlHU2NqxYwfuvvtuxMXFQaPRYP369TafcxyHuXPnIjY2FiEhIUhPT8fx48dt2pSWlmLs2LEIDw9HREQExo8fjytXrti0OXjwIAYNGoTg4GC0a9cOixcvbtKXL7/8Et26dUNwcDB69OiBzZs3i+6LL9MvIQoRoUEu24TqApoYZWJqInqazQfPI2utY8kKT9RokzIgUq3A5gHLJDWmX3unxdqdwW87Dly0DWNW7fGaES/m2f9o1ymXBZwB1+8pHwdqNMi/VfXbH1WCbeTcSvPFbTln11/Nc4MaUXQbsaqqCr169cKTTz6J+++/v8nnixcvxttvv41PPvkECQkJmDNnDjIyMlBYWIjg4MYbO3bsWBQXFyMnJwf19fUYN24cJk6ciDVr1gAAKisrcccddyA9PR0rV67EoUOH8OSTTyIiIgITJ04EAOzevRtjxozBwoULcdddd2HNmjUYNWoU9u/fj6SkJOa++Dv6QC12vDAEeafLVB/gnF1QjKfXHHDZRmmXvJQBUY4tITVlPhLOEUoWqb1mFn1MZ9uOno7rig5jC0vQaABOYK3D8p7ax4FGhzV6BTcdKnZre3Hp1uOoqb+G2SMSnbaRcyvNV7flKPnFfTQcJ/QqyHQijQZfffUVRo0aBaDRkxQXF4cZM2bgueeeAwBUVFQgJiYGq1evxujRo3HkyBEkJibip59+wq233goAyM7OxogRI/D7778jLi4OK1aswF//+leYTCbodDoAwKxZs7B+/XocPXoUAPDwww+jqqoKGzdutPSnf//+SE5OxsqVK5n6wkJlZSUMBgMqKioQHh4uy3WTi9yTlzBm1R7Bdmsn9FdFrIArGswcBi7axpwSvnR0Mu5NvkmxfkiNJ5FyrSnzUT48ZbQ6Ow/rOwk0TsIx4XoAGqfeMH6i3jlzqOKT4K4TFzH2g72yHlPoPbXO2Pzi599xpfaay+NFhAY59ajZ8+4jfTCiZ6zTe5VdUOxSoX6lhGxEwHHmNHmLvIPS87fXAuSLiopgMpmQnp5u+Z3BYEBqaipyc3MxevRo5ObmIiIiwmJoAUB6ejq0Wi327t2L++67D7m5uRg8eLDF0AKAjIwMLFq0CGVlZYiMjERubi6mT59uc/6MjAzLtiZLXxxRW1uL2tpay78rKyvduiYsSJ0gfC1WwBVitXeUcsmzlMhxhdhrTZmP8uFJo9VZsoiY4G8OQJ/2EdhcUOKyjaeCqy9eqRVuJBJX76lQxqY9UWFB2DM7HSu+P4ElW4TDQeZcD/ZfsMl5KTO5IE265onXjC2TqTFwOSYmxub3MTExls9MJhPatGlj83lgYCCioqJs2iQkJDQ5Bv9ZZGQkTCaT4HmE+uKIhQsX4uWXXxb+sjLhzgTBanBEh+mRe/KSql3FYoyUWIVd8s4GThaiRWSI+lrKuJpRi9HKaqxrNYCZg0tDyxpPLJhOXRSOdRKDq/dUSrme0qp65J0uQ3x0GFP7S1V1eHpNU8+V6XopM1fxro7ePaFFsS9ty1HYgjyQ9IMbzJ4928ZjVllZiXbt2ilyLncnCJZYAUNoEGZ8+YvNNoUat6jEeKo8kSljP3BeqKzBa5uPCv+hiNnD11LGPYXYiUBuo9XdiciZsR4VFoTe7SKw9egfEJvfoXRwdYOZw9p9ZwTbGa9ve5ZUCnvu7ukV6/C6uVOuh78n7sCf19V2pP27x7oo9gVNOgpbkA+vGVtGY2PtrJKSEsTG3rhpJSUlSE5OtrS5cOGCzd9du3YNpaWllr83Go0oKbFd8fH/Fmpj/blQXxyh1+uh10vXr2JFjglCSGWdAz+gOE7NVtMWFcv2iwbAs8O6yFajTQjrgfPr/HNMf3Oxin0rRuo2sD+vSqVMBHIarXJNRI68HCkdIvGnN7YzHwPwXHD1vqJSmCqFn90x/Tqgq7EF0zb7+zuK0Lt9ZJPr5k65Hv55jwoLYpapcAc+U1QNXlM58Kfvoga8prOVkJAAo9GIrVu3Wn5XWVmJvXv3Ii0tDQCQlpaG8vJy5OXlWdps27YNZrMZqampljY7duxAff2NlyknJwddu3ZFZGSkpY31efg2/HlY+uJNxEwQrnCWwhsTrnfqJveEhIJYWLR3OABvbT3uFV0rJdK7pRzTGxIBntJ/4icC+/dCSFpDrthFqed3Bm+s35t8E9I6tULe6TJRRoYnNY9Yr2F8dKhlzGkM7neNozFG6pYovy0ZoNXg1XuTJB1DLNFhepeLYkBd46grhBb4gO98F7WgqLF15coV5OfnIz8/H0BjIHp+fj7OnDkDjUaDqVOn4tVXX8WGDRtw6NAh/PnPf0ZcXJwlY7F79+7IzMzEhAkTsG/fPuzatQtZWVkYPXo04uLiAACPPPIIdDodxo8fj8OHD+Pzzz/H0qVLbbb3nn32WWRnZ+PNN9/E0aNHMX/+fPz888/IysoCAKa+eBM5g9szk2Kxc+ZQrJ3QH0tHJ2PthP548/+Smd3kaoFVe8cbulYsgpZiY8mEjgnY1nqT2xhgwVPGnTsTgRyGsCcmIrFGhic1j8Rew8ykWLzxYC+XbZ2NMVK2ATWwNTpH9IzDU4MTXP+RG/DvMzSQZVHMipILG7kW+MQNFDW2fv75Z/Tu3Ru9e/cGAEyfPh29e/fG3LlzAQAvvPACnnnmGUycOBF9+/bFlStXkJ2dbaNr9dlnn6Fbt24YNmwYRowYgYEDB+L999+3fG4wGPDdd9+hqKgIKSkpmDFjBubOnWvR2AKA2267DWvWrMH777+PXr164d///jfWr19v0dhi7Yu3UFoI78Jltu0stWUq8objZ+NTERGiHs+cEqrL/DFdfYPy6nr8bVMhdh2/iPkbPLsq9aRx585EIIch7ImJiPVdzhrSGWsn9MfOmUM9tqXDYvi3DA5AcflV5J68hM0Hi/HUp3kuWt/AfoxhEWO2JjI0yKHROXtEIt59pA+iwnQ2v481BOPdR3oLPhMRoUHQwPX7zJqhKcc4qvTCxp+y19WCojFbt99+O1zJeGk0Grzyyit45ZVXnLaJioqyCJg6o2fPnvjxxx9dtnnooYfw0EMPudUXbyGnEJ6jOJOoMLbBTE2qxjwBWg20Wk2TMiDWeCN4XM70bj7u6mpdA1roA13qC3246xQ+3HXK5fHkvh6ezpR0ZyIQil0EhA1hT0xELO98TLgeaZ1a4cLlG/UHPRGPx5JFebmmAdO//EX0saWOMSy1UEf0jEVGkuMMQK1W4/KZeP3+HgDg8n3OPXmJqa/ujqOssVSu4jWFYjlZ+5hTWKKIhqE/QtmIPoAcEwTg/CUVCh5Vq6oxj1KTnxyZZu6md4vVFxKDXKtST2dKuuvpddcQ9lTJldF922PJll+b/J4fA2qumW2ERT2ZJeaO5IkzHHkU9xWVMgmTLn+kDwZ0iRZs5ywDkPWZcPU+e0IdnnVhYza71gwTSuzolxAFY7heMBFi48FijEgqxoieFCgvBBlbPoK7EwRrCrWjlSoHYHRfZSQt5ECJyU+uTDN30rul6AuJQS5Ppae3HOSY1NwxhFmyYa3j5+wRMuKFDGzDdWV0eyPE01limUmxGNotBv0XbkVpVZ3bx3Mk/8D6zFhn9kpdJLl6JuyPeVfPuCbHlGtR7ArWhY0rzTBH2D87AVoNxvRrzywIm5FE+n5CkLHlQ7gzQbCmUEeG6RwOnEu2HMe6n86qUl+Fj+twtgIWu6KUkvIst8SCO/pCQsjtqfR0cV05JjV37hd/flflW8qr65FTaGrynAgZ8UIG9l09YvDz6QrYS7QA3hG3zTtdJouhBQAbfinGC5ndbfrNKvzLtxOzSHL2DNgvjsQcU2l1eHcWLK7GEkfPjhhB2Oam7ycFMrZ8DKmeEtaX9J5esbh4pQ4bDzYNtCxWqb5KTqFJMJuSdUUpJf5ICeE/d/SFXKGERIA3iuu6M6k5i1t89d4kjOgZx3T+4YlGQQPf0XPiyohf/khvLNh0xOWkuPGQaxV5T8cnyhkg7bDfrKsNTtwiSeidta7D+JGDGEhXCy8l1eGVjJu1f3bEnIvlOfBnzT8WyNhqJrC+OF/+dBZV9Wann3NQV1kY3jhyRURoELO4qdj4I6WE/5TK8lGi/pontk8cIWVScxW3+PSaA3jq93LMHiFcC08olsj+OWEx4v+6vgBljIWThfBUllh0mLyizvb9ZhX+vXC5Bov/d4xpkZRTaHL5zv5lUAL+s/+cS48df8z5Gw6jZXAQLl6ptXn++EUxb2BsPHheFgNDTD1NqfD3QIwgrND8Qkr0ZGw1G1hfHFeGFo+aysKweIDKq+uZ+ysm/ojVCza0WwzyTpeJWtEpsYKdlt7FZbaWK1hqvXmjuK4YTy/L1ux7O4rQq21kk4Bf++9vXdLKFfzzxGLEy2VoAR7MHJZ5vWXfb9bvUVpVx7RI2nPykqBG2qofi5jOyQEwVdY6TVJQwsBgqQTiLvw15wVhn15zwGV7IakUUqJvhIytZkKAVoP7km8SlAVgxdOZfe72g7WdmPgjVi+YfQAxy4Ar9wpWA2DdT2eRNbSL6L9lnTTUXlyXdWv2hf8ctAn4dfT9WwYHMJ2Tf5485WnydOYwq7aUEM76zfIeaDXA72VXmc6T+9tFRbbneXgDYuLgBLy/o0gRA8PVwmbOyO5YsOmIpHHD0T3QMry7rrzWnpaFUTNkbDUj0hONshlb3sjsc6cfrO3ExB9tPHie6Zj22xEsAy6LlpEYpMbyiF2Vqrm4LqvBc6X2GpZtO4Fn07s4/f6XaxpcHsN+4lLC0+TJLVtnyPG9XPXb+j1whpkDPt59SuTZlMHaO6akgeFqYeNKM4xz8P/8vwHbeyBHiIanZWHUjNdqIxKeh9dOcZdWYTrRmX1KKYvLXRpHjPq71ImGVcWdtSSRGMR4WPytPpqY+/Xx7iLUXTNLygh1NHGxqK6LJVRn613zZMkeHtbv5epzoX5nJsVi+SO94Y79yI8DnprQXb0ScpW6sa+nyT9rzsYN43W1/GnpN8NgV3HD0T0QE6LhDFKivwF5tpoRYrRTXLHg3iTFMvvEokRwNmv8kTtbfawrOn4FuyTnVyzbfkLkWZoixuDwt1VpSodI5rbl1fX4V+4pSVtOjuLU5PZUAkBVXQOmDuuChNZhXtuyZfleTw1OwIZfiptkf96XfBPSE41M/Y4M07s0YFxhPQ7079hK8QBzVpQ0MBx5vsqq6poInbpS3pfDUPK0LIyaIWOrmdG+FZt2ijOeGpzArBbsqclaieBslvgjOSZQlgEtQKvBgM7RbhlbUmJ5/G1V+pNIT8Lp0mrR55gzsjueGJDg0HhQQnX9X3tOY99f070a75KZFGuJUbInTBeA3u0j8UJmd7di+dx5xuzHAaFtSU+htIFhvaWfXVCMKWuabodXXK3HW1uOo6uxZZNxUg5DyRuyMGqFjK1mxOaDxZj7dYHkv182ujfuSmbTIQI8O1krEZzNEn/kbAKVkjLtKomAZdAyhAahorq+yedSPXz+tirN/e2iqPYdokJFnyO6pd7lNbZ/TqPD9Jjx5S8oqZTmaVGDoGR2QTHec2BoAY3et0mf7sdKN7c33XnG7Ovz8u/si18dYnpHpaDVABzneAHmaQND6g4DP+YILQzKXEhkeEsWRo1QzFYzYeHmQjy9Zj8u1zgvYuyKiJBA3Cmy/pU3lMUdxTAoTWZSLHbOHIq1E/pj6ehkrJ3QH3tmp4uKJcsuKMbARdswZtUePLsuH2NW7cHARdssMW0ssWQP39oWhtCmRcUjQoMkxfLIHQ/nfdifh1hDMB5LixcdZ8XyLFs/pwO6RGP+PY7vKyve9Cw2mDnM33BYsJ27sX38syiFksraJvGhmUmxmHPXLZL74wzN9Z8JgxIs/7b/HPCsgSFmh8GaAK0Gc0Z2Fzz+gk3SYk+9EWPoTcjYagZsPnje6cqTlfKr10QHdPrfZO0ce0NPF6hlDrRnTSJwNWjx2ziOhDal6jeJSRbwBcR4f0b3bW9zD4WQ8iw3mDnknryE2mtmTE2/GTHh0owJOT2LfJ++zj+H3JOXBA2kfUWlgsWKAfcDwlknfkc4S+Y4c6lKcn+cwRsQs0ckqsbAYDXGd534o8n9jmQQrWW5t5lJsfjh+SGYM7I7/pzWAXNGdscPzw9pNoYWQNuIfk+DmcNLbmwdWiN2Bd3cXcgssWRiXfyOtktTOkTiT29sd7oN5U4igrfESt3B2XZs/46tEKoLQHWda9kGAIiPbtxC5OORVv1Y5DRAW8qz7EgOxRiux7PDOuOT3adRfpXNQJZzsSJFokXMmOCuB45l4neGI0X/tfvOuNUfHutg/5QOkcg7XYav88+hTctg/PD8ENGCxnLDaowv234S/9l/zuZ+s4r3CrVz9Gx9sLNItWOIEpCx5efsKyqVLS7h1EXxAcNiJmt/rJ0lFEsmJYnAPpYs9+QlRRMRnBl41pOKWu6VkMHw1OCOTNm4/ASVXVDsUJzSGiHD0/65LquqcxisXFJZi6Vb2ZMgNJBvsSJV5VuMV81dD5wc26XWiv4sHjkh7kyKwbJHUiwe6j+9sd3hs3dv8k1un0sqYrKm7e93KaNorat2pCDfCBlbfg7ryoSFt7b8iq7GFqJfDJbgdX+uneUq0F6OJAJPJCJYf4fNB4udquJ7U0GeZVDPGtoFH+8+5bKuYURoEPolRDGV94kKC8IPzw+BLtBxRIaj51qrcRw4LSaiKSI0CK/f30OWd8MdiRZeu0/IcJHDAyfHdqnciv7fFpQgp9AEAE6fvUmf7se09C6Ij/aORIeYrGn7+x0VpmM6h7N2pCB/AzK2/ABXHiHWlQkrYl4MVk9Vc175yJFEwHqMi5drJXui+Hu56seT2Hb0jyafF1+fVCJCg2wMGU8ZzKwCrMMTjXj9/h6Y5CL1v7y6HjmFJhhCdIKZWKVV9cg7XebQmHb2XMuhAbt8TB8M6BLt/oHgnkRLgFaD+ffc4vJ6AsIeOJaxol9CVJPnixWlFP01wPUEAY3LZ8/am+qNRaQY2RHr+200hDAd31k7f9PqcwcytnwcIY8Q68qEBTEvBqunyh9WPu5sf8qhQ8NaP27BpiOWf4sZ8B3dS2fYT4SeMphZ1K75Z3d4ohERIUFO46L4Z+6FzG5M53bkJWHxikmBfx76yzgxuesZbfRct0FO4QWnf3vgTJnT+886VuQUmiQbWkBTRX9WeRZX8MWoxeCtRaRYgeQLl2twZ1IstBrXCwStxrlgsFxed38IMaFsRB+GJYuNdWUiBqEXw1m/ih2U6JGalqwWhCQbhJAj48/VMXjsB0vWcknO7iUrnirrIyaQd+q6/S4D0PlnjtUr7MhLwlr0WgpyJ5VEt2ALPHfmDaq7ZsbWI84NLaCxVmDdNXOT37Nm4rLU6XOGowzAAK0Gr96bJOl47uLNUle8QDILbVoGI+90maAn1swBeafLnB6D9VzOcHeMVQtkbPkorNsmKR0iERXWVHvJHVy9GEIreg62g4wvq5S7migmfbofS7f8ypRCL4cODX8Me/kAZ3Myy4DfqKHkvnfGEwYzq2H0xb4z+OagialtVJhOsnSJUs/rxMEJsnpDsguKMeOLfJdthGQt/pV7imlC/lfuKZvfiam9yWq8tgrT4d1Hetto3u2cOdThNRvRMw5PDU4QPKYSeHMRKUaSZ0sh27vi7Hl3V/5H6dq6noSMLR+F1SOUd7pM1hVcREgQzBzndIIWs50D+K5KOctEsWTLceaVmCNhVGeThGtse+ROQdxl247LmmChpMHMul2+R8TkZjSESPY6KvW8bvilWDZvCD+RudoGc/U9eU2u739tGsPnCPvyR2K82qzPzksjuyMjKRZmM4fjJZex68RF7PnN+WJn9ohEvPtIH0Q6EANmQYNGyQ5juLQi495YRLJ60wHgq/xzTMd09ry747kXY4z7AmRs+ShiPEJyruDKr9Zj7Ad7nRoPpoqrTMfh2/mq8KnYbSKWlZg7CvgsE6czHD1L2QXFbhcst8d+QBYroOkK1u1y1jPwz5xUr6PQcw009TqyeKDl8oawxpQZQoIwNb0LhicabX5vvbXz43G2Mkj25Y/EjGGsxuuZ0qtIeTUHYz/ci2XbT2LZ9hMY+8FepLya4/DdazBziAzTYe5diXiwjzh5Bv72zb/nFslVAKLd0A5zB5bnmlU2qFWYzuX4LPUd8vUQE3soQN5HEesR6t0+EoB7KvLWOAvyLHVRJ8savp2vCp+KXZEqGezvbjC2IyNIanyMK6xrqMkt9cFSxy00KADV9cKCpoDtMyel7maAVoN7esU6rdygAbBsTG9EhuktxzRV1mDa5/mCfZPDG8K6WCi/Wo8lW45j3U9nLffGWZalK7Qa4LG0eJvfiRnDWJJAQnUBWLLlV8ffo7oekz7dj3cf6Y0RPRvruzp6BiOue7hYAvHt9dUkFRn34rAm9FyzPmf3JscJjmdS3iFfDjFxBHm2fAzeG2CquIqoMB2TR0iJydOZGzeKMdjWup0v1s6Ssk2k1EpMajC2M6+hUsHdfA01OeIw7L1iAHCXQO3OXu3Cmfr5YJ+bmjxzYr2OvBiqMyYOTsCInnE2xzQyluuRY4tS7ATF35vNB89LMuwnDEpookUmxqvNL8pcnZelMkDW2gPYfLDY6TNYXl2P8up69GlncHmcaeldmmzz8yVp+sU7zsxzxEWZpXnE4uq5Zn3O7L2eUs7lCF8NMXEGebZ8CNYUfHuPkJDCuFQcSUGwThj27aSsfLyJGFVme+ReiUk5niuvodjjRYUFIa1jK2w65DqYtriiBntOXhIl9eEo5Tun0NTkPTAEB6CyxvVke6LkMtP3mX9PEnJPXpL8HLJ4Gjf8UowXMrvbHJflmYoKC3KaZi8GsRMUf29e+rpAlFyCVtNoaM0e0bTGpFiv9vBEo2SdLR4zBzy9plEPztX92X+2wulnGgDrfjqLrKFdbH6fXVCM+RsOi9rKV7OhwPI8KhniIYcsjpogY8tHEOO6t3dvK+1mtT4+y3aOsxfUldK62hCjymyPlLJHro8nvqCufX1Ga4OGVQqAZ+n/9cZTn+Uxtc397SJzHEbF1TqH2zyOJtsKAUMLAP6ousbUx8FvbHeojs/qYRWTJGL/vI/u297pVhjQKKL6pze2uy2KKWWxwF0/Pwu6AA3u6RWHv93f06m6PmBbe5Kz6ojmupFm/R33FZW6ZWhZ485xHC0ypWytqjEW1Rpvh3h4+/xyQ9uIPoCYmJyosCDMGdndZpASE4Q5K6MrIkLEZeZYr874F0QDx9knctZz8zbOtj+FWPfTGVkzysQGss8Z2d2yBeJIw2bGF/kI0wUwH2/NT6eZtnAaYbvvOYUmp9s8SmMfdyg2zVxKujx/H1wZWlL74wgWbTZ3qGvg8J/957DtaInLdvx2q/3rYOaA93cU2XxHOTNj5YC/f1JjJn1hHPR2iIe3zy8n5NnyAcTE0JRV1WPKmgNYodXceBBFvM9HSy67FHy0xpkb11lpiMiwINyXfBMMITo0mDnVDzQs2G9//vjrRfx7/+8u/0aO8hQNZg57Tl7CrP8cEv23UWE6S+Fch2WSRGY0bhEQtOSJCA1CWqdWTOrV6/PPy66+LhXW7U3+92LT5cV6RcQmWzjrq5gSLta00AeiqvYaU39d9ZHFSOH/PqfQhAUbDzP30RNsP3oBx0uuwBASJDpMY1r6zbIaCqwK61KU2Pkxbs/JS8j97SKAxh2I/h3FjV9SVeB9LcTEGWRs+QBitgH5gWvWfw6hZXAQ+ndsJSoIk91D0Yiz1Zn1C/rp3lP48fhFlFbV48Ndp/DhrlN+U2QaaLr9KWRsAbb3VOwgJKZ8jiMWbDoCfaAWCzYdkcWgqW9gO8q42xLQv2Mrl3E3GjQa5axZrZ5CaHuTf54NITpR6fJSvSJ8f1bvKkJ0S73T50Yo69N6IjNVXMUrGwtRJuA9DAxgm+SEynuxpva/s/U4lm49rhrjm2d9/nlJf2cM1yNraGfZ+sGa2etOBrB9nOSy7ScQFRaEV+9NsmR3ytFHZ/hSiIkzaBvRB5ASRGmthyUmpqdvPFsMQaswnaAbN6fQhClr9+PbghJcqbU14vjCxUu3/OozonQsiM2gcbSN1/e1HGw+6Hggd7d8DtAowfD0mgOKlZNxRKguAFlDOwvWt+MA3JcsTu/Ikzjb3uS39li3EO9NbpygVu8qcus+LNh0xKlwLmvWJz+R3denLZ64LV7wnOXV9ZiafjNzuIGzxSLrIlKNhpY7jOnXXrZjsd5jdzKAnf1taVU9nl5zAAs3u8509ycVeHfwurE1f/58aDQam59u3bpZPq+pqcGUKVPQqlUrtGjRAg888ABKSmzjAM6cOYORI0ciNDQUbdq0wfPPP49r12yDYb///nv06dMHer0enTt3xurVq5v0Zfny5YiPj0dwcDBSU1Oxb98+Rb6zWPhgVimYKmqwZMtxGEKEnZiRoUGIDQ92WuLFmpfvvsWloZVdUIxJn+4XjLFZsuU4Brzue3WunCEmnV3sINZg5jDrv4dkKZ/jaUYkNaaHC0mQRIQGYWj3GE90SRJf/Py7S0XrdT+fZTqOIUSHgYu22RQHdxfryUuq+nZ8dBjTueKjQ7F8bB+mts4WIKwLEynP6/291WuwL9lyHAMXbcPmg+fdEvVlvcd118ySldhZxpz3dhRh80HH47e/qcC7g9eNLQC45ZZbUFxcbPnZuXOn5bNp06bhm2++wZdffokffvgB58+fx/3332/5vKGhASNHjkRdXR12796NTz75BKtXr8bcuXMtbYqKijBy5EgMGTIE+fn5mDp1Kv7yl7/gf//7n6XN559/junTp2PevHnYv38/evXqhYyMDFy4wBaPoiQBWo2ghpAz+PgOjUbYgro1PhLPrDsgWOcMAF7aUOCypp4YXS9Tpf+scMSUwhDaPrIfxJZtO+6RAHElaBkSxBR7WF5dD3Bsaure4Eqt84xGDkBVrfA2fERoEN7a8qvsnkXryWvPb67lXuw133jdMtZ38NTFavTv2Mqt6g9lVcppTP2pa2usfLSP5EWqPZm3yLsAKK6owdNrDrhVXJl1G/ZfuackK7GzjjlzvnY8H/ibCrw7qMLYCgwMhNFotPxERzdWJa+oqMCHH36If/zjHxg6dChSUlLw8ccfY/fu3dizZw8A4LvvvkNhYSE+/fRTJCcn484778SCBQuwfPly1NU1xn2sXLkSCQkJePPNN9G9e3dkZWXhwQcfxJIlSyx9+Mc//oEJEyZg3LhxSExMxMqVKxEaGoqPPvrI8xfEjs0Hz+PDndLV3zk0TmIt9K69W1uPXGBeRZZX12PPdTFJe6SIYnIA5m847BcrHNZSGCzXiB/EGswcPt51SqEeK8/Hu04xb7FdrKpV9VaiHCj1lPOTV66Td9OeC5drbLayvy1wnT3I89aWX5FTaHKr7p2cXj172rQMtqk3uuT/ejHXz7TnqcEJuLOH8rGlYrfVWLdh7WtSsh6vwcxh5Q8nmf72UlWdQ4PJ31Tg3UEVxtbx48cRFxeHjh07YuzYsThz5gwAIC8vD/X19UhPT7e07datG9q3b4/c3FwAQG5uLnr06IGYmBsrj4yMDFRWVuLw4cOWNtbH4Nvwx6irq0NeXp5NG61Wi/T0dEsbb5FdUIyn17B5m4RwtSoHXBctdkRjZkpTpL44pspaLNsmnKnmCwxPNOLvD/ZC1pBOyBrSGZ/9JdVGcZr1GvGD2L6iUuYsUTWigbiitumMqtS+xoN92or2ToqR4bgB28t86mK1pBhADjcyBZc/0geRdoaMu3Xv3MEYroeZ4/B1/jnsKypFv4QoGA0hopMuosKC8O4jvTF7RKJHxEfFbqux9sm+JiXr8facvISr9WamvwUcj2n+pgLvDl7PRkxNTcXq1avRtWtXFBcX4+WXX8agQYNQUFAAk8kEnU6HiIgIm7+JiYmBydS4SjaZTDaGFv85/5mrNpWVlbh69SrKysrQ0NDgsM3Ro0ed9r22tha1tTdc4ZWVleK+vABK1aiTD8cbCO68OEu2/IquxhY+naXoKPPmP/t/t8m8EXONlFr1xRqCcVfPWKz6Ub6amc7gBTGjwnQoq6pjUoSWqtCvZkJ04te3YTotpqbfjLNl1eA4Dv/ac0bwb1LjW+E/hnMuDRpjuB5r952RfH2LK2qwbNsJrPvpjI0h40jrzx4lPRk118wY+8Fey79jDcGWmEFWpqV3QdbQLhavXEqHSESGBglmarqLUAanNawK64+lxeODna4TMSJCgmDmOBtJns/2nhLVd0djmrdV6NWE1z1bd955Jx566CH07NkTGRkZ2Lx5M8rLy/HFF194u2uCLFy4EAaDwfLTrl07WY8v5+pPCUUSZ4OBOwH9gG8HTLJm3vRLiGKOS2rTMlj2lZ8GjeKmG50EtirFqOtZeELbTtaxb/6EFLmAC1fq8drmI/hn7mn8a88ZpgSW5/9zEEk3ua4F2Tc+0m2h0CUOYs94rT9H22F8bNhxxtJJYnBWRNpUUYMPGbfgW+gDsfLRPng2/WaL0bH5YDH6L9zKbGjJMdayGKNC7wiHxnc873QZuhlbujyWdfZ6dkFj7cjNjFvKwA0pE7F9BIB7esX6nGaWFLxubNkTERGBm2++GSdOnIDRaERdXR3Ky8tt2pSUlMBobFypGI3GJtmJ/L+F2oSHhyMkJATR0dEICAhw2IY/hiNmz56NiooKy8/Zs2yZSKyIWf2F6QKcqrYD8seI8FsbjowidydKXw2YFJN5E6DV4L7ewvo0Wk3jqtpdA9aaMF0ApqZ3gSFU51H5B6Bxe9VRPFtMuB5T07ug9poZu45fxK4TF1F7zYyp6TfDIKKiQVRYEIZ1a90kPifWEIynBifI8h3c4XINW8kgV7CsQ0yVNcgpdJ3cs+3YH273xRHOtsOsY8OWbWeLBWIhTB+AZ4d1RnCg4+1WvgdajbAhdKX2Go4WV1oyBF/bVIin1+wX3ILUaBpju1ZKqCjhCDGLK2fvhy5Qgxf+cxBjVu3BdsZ7bbouyTPrv+LEkhfcm+TUYOJLMjnDvlKAv+L1bUR7rly5gpMnT+Kxxx5DSkoKgoKCsHXrVjzwwAMAgGPHjuHMmTNIS0sDAKSlpeG1117DhQsX0KZNGwBATk4OwsPDkZiYaGmzefNmm/Pk5ORYjqHT6ZCSkoKtW7di1KhRAACz2YytW7ciKyvLaV/1ej30enF15MQg5oULCtRi+YO9sGCT7faV8boLnXVlx0pVXQPGfrDXqTDd8EQjDCGBqLgqbXLxxYBJMZk3FVfr8OHO04LHNHNA3ukypHVqhTkju+PpNQfc7mdVXQOWbDkuuiyTu4TpAmDmONReM+PvD/YCNMDFK7U4dbEaa/edcVp2KDKUrZ8tgwNRWlWPrUcbJ5ao6xUL0hONFsHP3u0jMeu/hxzGTfH110J1AaLFfb2B2Jqc9rBkTkrFWnT1iQEJFn0yJfzV1bUNWLpVONaT1Vn+FsOx7OGulxda8Wgkfnh+CP6VewpFl6pwtrQaP/zqOLbVGZGhQQ69RPbixxev1OKZtc7Hg7prHOquibvH/CUSE1c4YVACRrjIlm8wc9jwi2tjirUagi/jdWPrueeew913340OHTrg/PnzmDdvHgICAjBmzBgYDAaMHz8e06dPR1RUFMLDw/HMM88gLS0N/fv3BwDccccdSExMxGOPPYbFixfDZDLhpZdewpQpUyyG0KRJk7Bs2TK88MILePLJJ7Ft2zZ88cUX2LRpk6Uf06dPx+OPP45bb70V/fr1w1tvvYWqqiqMGzfOK9cFYCvqzFNeXY/IMB12zhzaRI18X1Gp7MYWD789xgfD8gPCrhN/SDa0AN8MmGQ1EE0VV7H4f8dEHzdSRI1LFjwdcF993UDniQgJwsAu0dh0sNjlJMy6fWPvNSqrqsdHu06hr5WyOq+Y/uy6A03Oy/+/pwytlsGBbnm6fGGjfcGmI1j142+ouWYW1d/gIC3G9G2HiFA93tryq8u/VdN1mPXfQwgOLHRre9bR93G3aoRSTBgUj7+OdL2LIWYRyoemSC3to2a8bmz9/vvvGDNmDC5duoTWrVtj4MCB2LNnD1q3bg0AWLJkCbRaLR544AHU1tYiIyMD7777ruXvAwICsHHjRkyePBlpaWkICwvD448/jldeecXSJiEhAZs2bcK0adOwdOlStG3bFh988AEyMjIsbR5++GH88ccfmDt3LkwmE5KTk5Gdnd0kaN6T8Ntxkz7dz9R+14k/LA/nXT3jLA+nGKNNLNZ12sxmNPGsScHZ/r/cyP1CsxqIu0641kBydlxf9PZZYz+JlF+tVzRmjD/f7P8exKFzFdBcr+lWUa3seVm5r3ccNh40qa40kdyIrbUJADX1Zny8+zSeGpyAFY/2wYtfHWIqg+RNeIkdwL1+llfX2xgeYmtneor7e8fhryNvEWwnVv7B3dI+akXDcZza7qHPUllZCYPBgIqKCoSHuw5OFcPSLb863WJxhjE8GPPvufFw8oruvsCTA+Ix927hl9gdlHihG8wcBry+VXByiQgJYvYqxRqC8cPzQ5B3ugy7Tvwha6xLc8Xd7Tc5mTAoHqt+POXtbqiadx/pg9oGM6Z9nu/trniMpaOTcW/yTWgwcxi4aJvqPFoAMGVIJzyf0U2w3a4TF2082s747C+puFxT79KwvP3maAzq0hqPpcVDFyhvyLlS8zeP6gLkiaZkDe0CY7i4bTVTZWOgIx94mJkUi7t7+oZ20XCFNZaUqtUVoNUw1T0Ts313V89Y9F+4Vfag4uaMWgwtDYD/7GfTHnOEPtC3t1VYeWn9Iew8rkwwv1rhvdlK6pG5y29/XGFryPjCmRuEi7J//+tFLNh0BN3mfCtYk1FtkLHlAwRoNZh/T6LDbEMhZv/3EBrMHLILivHNQTYFb29TpuC2itK1utozCggaQoJc3kutBkjv3garfizy+22m5gqvPSaV2mtqMRuVpbS6nskojQoLwv8b2tkDPVIO+xJHng4dEDO/7C0qYxonLzKWZdp7ij28wsw1ljPzJYOLjC0fwVkJGCHKquux+8RFlYuj2rJgk3I6W0rX6mI1jIZ3b8ycdTa4jRsQjy1H2OtyMpS+JAi/prSqHl/8fBbBMm8vKQVLiSNPJwpFhAZh2ehkpralVXVYvatIcKxm/w7iB7FVPxah7hq7yr038Y2nkgAAm1pfS0cno188WxD5f/b/rlpXtCOU1NlSulZXVAu2jMEBXVpjxaN9EBNu2z4yNAjjbuuAz39i02wbntgGj/VvDyUjL4d3b4PPxqd6XCqCldT4SG93wS1CgnxrGA7TBWBkDyOTuKqnMVXWosYLk6/YSzF1WBeXtVN5+iVEoWWwlHJN0tAFaBDJOIYBjdmmQgW0+QQtoYLlQor5jjBzwL9yT4n+O2/g9WxEQhwB2saMqgYzhxcZheeKLjLurauIb62U1uVM+T11sYqpHctqzFE2I2tsnTE8GBVX62A/TJdV1+Pj3cL6WzxCwpVy0NUYjv6dWilThkAG9p4q83YX3EJM/Tlvk5kUg+WPpCBAq8GCjYfx4c5T3u6SKhC71kloHeZQpsd+rAvQapDSPhLfi9TqkkrJ5Tp8uod9/AEaF8fW8j/28Fn1kz/d3yQ5xdqb179jK0kluoousY3p3oaMLR9lX1Epqhj1gH67yFb1XU38M/c0/pl7WtaU3+yCYsGsTvv6fIBjoyqn0OQwm3HOyO6CMhuxhmCUVdVhyhr1pXM74sSFK1i9q0h0AWXC/3g8LQEBWg0azBy+OiA9uL+506ZlsGXhLERap2iPGVsA8G2BtNheV8KkvIr8qh+LbLzwGk2jKCo/vjszylyh0jVgE8jY8jH4if9bERlzl2uuoYU+EFdq3S8V4mnsRVMd4Uwvy/r30WF6zN/AFrdmHTPhSCIiIjTIoeFhqqjBlDUHMHFwAt7f0Vjc2dGAcUdiG7z0dYFPGFoAkH3YhOzDvpFcQSjLtQaz5b1Su+6VWtGgsQSXmPaeRqthV9wHhAtoZxcU4/0dRU3GPPN15f3e7SORmRRriU0WI+Dau51vhBGQseVDuKMi7IuGFmArmupo1eRML+uunrH4z/5zojP5pqbfbKNN5kjzxZmHh+/rhl+KsfyRPnhxveOyMJ/knhHVJ4JQC499tA+xhmBk3uI9sWdfh8ONElws/F5+VdkOOUBqfpJ9rGuDmcOek5cw6z+HXC4urcd3vsrD6l1FWLDpiOA5YyNCpHXWw/hWZGYzxpk2VHPAWYbg5oONQq3216S4okayZEJ8dKN0Q4OZw/wNrjVfXPV129ES2nYj/BJTRY2ouEKiKWIScDowysnIzZMD4hEVJi4pxjrWlS88PvbDvS61BR2N7wFaDZ4YkIAIgbqozupIqhEytnwAqRO/v2E9QG0+eB5Za+VXxOcHi2XbjrtV3+zfbohVEoSaae7jkByIkXR4LC1euY64YHiiEXtmpyNMz5YNGWFl+EhxDkjJAPelZ5GMLR/A3YnfX+AHqOyCYjy95oBkV7crth0tYQqkJwiCkMrFK+z1InWBWnSM9qx3K+p6fdptR0vAWtHvibR4S6yskBK8I6wN0AYzh492Cifl8HUkfQEytlSOuxO/r5ToEYIvTs2/yEqx6seiZlWDjVAnYTrPaSsRnmf+hsPMws2bD573eEb5q/cmIafQhMmf7kd1HZs0Sd/rXi2xJYbsVfOzC4qR8moOXtssHK8FeF5lXyoUIK9i5DAsdvz6B4zhwT7vGbs3OQ4BWg1yT7KXdJCKL+keEf6JLlDLLO1C+B6XquqcZu5Z02Dm8Nf1BR7qVSMpHSKQkWTEwEXbRHmneG+dGOOHl3gY3bcdNh48j1MXq7Fky6+i+utplX2pkLGlYuQoQlpR04DObVr6vLF14XLji+zr34MgWCjzgeQKsfIAhC0sRsm+olKPPwt5p8uxbNtx0XMPb/RsKWSXiTFcD4CXsnvjSBNRzdA2ooqRyz2ad6ZcluN4k40Hi7H5YDFKRcQ6EAQhP5rrPxMGJfiMoKQaYfHIeGuL7ONdp0S157cBNx88j28OChtbESFBmDqsCyqq693K2rbWRFQ7ZGypGF9xj3qKl74uQESoztvdIIhmDV/Db/aIRKx4tA/0PlL4WU3EMnpkvDUHuJJqcMQ9vRq1CV/6mm3L8/Hb4vH5z2clZxOG6QOw/JHeslQW8RS0jahi+AKeYmtF+SulVXUorSLPFkF4g8S4lpgz8habGn7DE40I1GpAb6U4WD0y/RKiEBkaiLJqdYtSv7ejCKG6QOaqAqcvVbkVIlNV24AFm45Ae10E1RegJYmK4Qt4Eje4eJmGdYLwBoXnL6Osqs7GSHhn63EK5BdJ3w6RzAZCgFaD+3u3VbhH8vDejt+Y267PP+/2+fhSbtkiStd5EzK2VA5fKyoiRJySr79y+Hylt7tAXKdrTAsE0QjSrJjzdYFFsmDh5kK8tZX06MTCidynSE/0Dfmeag8b3fxVfPmbQmYZDW9CQ6UPkJkUi+Vj+3i7G6oghPSHVMOxkisglYzmBS9ZsPngebx3vdg6IY6bIsQJlCa3i1CmI36As1JuaoSMLR+hf8dWiDUEN/vsn5hwvbe7QBDNGlPFVeZAaKIpiXHhotqv2Ut1KIXwBWFTMrZ8hACtBnNGJkoOlI/ykyw+9TuLPYO/VAYgfI/GRBX164CplTYtxS0YT5d6Vj3eF4luof5FOBlbPkJ2QTEWbJKuJt8xOkTG3niP/1CBZwBAz7YRiDWQNAjhWTQaINJPFm7ewmgQNxZ3iPJsXUSp6AI03tt58YFVOBlbPoCUCur2nCu/KmOPvEcNBQkBAKJbBmPOyO7e7gbRzOA4oKy6ztvd8Fk0GiClQ6Sov3ksLd4rRozYc04a3EmRfrBw0QckgcjYUjlSK6jbY6qkAdKfaNNSD0MIeRgIzxMVpoMhhCQapcBxQN7pMlF/owvUYmi31gr1yDFRYTr8ZVACc3tdoBbdYsMt5Xc8jS8IgJOxpXLkqI8I+ISXlRCBuYHDup/PeLsbRDMkOkyP6lrS1pJKjojagTw920bI3xEX9IuPxKw7uyOMMfs7SKvBlDX73Sq9IxVWNX5vQ8aWypEzy8IfMhkjaEUNAHjqszx884tviPkR/sWkT39GvQ/oGqmVr/PPi9KFyi4ollSo2R1Kr0t8sArWVtU1eG1BP7pve5+oj0jGlso5dbFKtmN1jPaNQEtntArTYUDnaG93QxV4WkCQIHiqKG7SLXitMhb4MBJPU1hcCVOl+uUUAKDiqm+EyJCxpWIazJysGiu33GSQ7Vje4OW7b8GuE5e83Q2CIAi3YN2xkCuMRCxXahtQekX9QeeAeE+htyBjS8XsKypFyWX5rHatRv2uVmd0iApFZAud6Gr0BEEQaoM1oNubYp1ny6rhC1OGGE+hN6EAGBUj94t2U6Tvam2dLq3GlM/2e7sbBEEQbqEBUFbFtoj2Zpbd6t2+o1xPCvKEW8j9ot3WMRpRYb5b0Jq8WgRB+DocgClr9iO7oBgNZg65Jy/h6/xzyD15qcl2mLfqIvqAQ8sGkn7wQZYvX474+HgEBwcjNTUV+/bt81pf+iVEyaZnE2sIRt+EKKQmtJLleARBEIQ0OACz/nsIA17fhjGr9uDZdfkYs2oPBi7ahuyCG1nG3qqLqP4IqBtoIF4o1huQsWXF559/junTp2PevHnYv38/evXqhYyMDFy4cMFrfapvkOexT+kQiT+9sR3fFojXeCEIgiDkpby6vknGn6miBpM/3W8xuKguojAcxAvFegMytqz4xz/+gQkTJmDcuHFITEzEypUrERoaio8++sgr/dlz8pJsKf4bDxZ7JauFIAjCF7i/dxwiQrwbZsEvrV/+phANZs5n6iJ6G4rZ8iHq6uqQl5eH9PR0y++0Wi3S09ORm5vr8G9qa2tRWVlp8yMnu3+7KOvxCIIgCMf898B5PNy3rdfjlTgAxRU1WPXDSTyS2sEr/QnyMcuAYrZ8iIsXL6KhoQExMTE2v4+JiYHJ5HjrbeHChTAYDJafdu3aydqn82X+UTyaIAjCF/j859+x/JE+iDV4f/J+/X/HkPzK/9CnQ4THz+1LurVUrqcZMHv2bFRUVFh+zp49K+vx4yJ8V6qBIAjC1yivrsfxC1ewc+ZQrJ3QH4lxLb3an9prHPJOl0MfSFO1IzQA5t2dSOV6fIno6GgEBASgpKTE5vclJSUwGo0O/0av1yM8PNzmR04CA9T/ABEE4Xlo8lWO9388iQYzh7ROrTBn5C3e7g4A4JrZh1xNHiJMH4AVj/ZBZlKst7vCBL2x19HpdEhJScHWrVstvzObzdi6dSvS0tI83p8GM4dl2054/LwEQaibqNAgBPrASt5XqaptQP+FW5FdUIx+CVGIDPW+NmGDudG4IG5wtbYBwxMdO0LUCBlbVkyfPh2rVq3CJ598giNHjmDy5MmoqqrCuHHjPN6XHUcvQCbVB4Ig/Ij2rUJQRYXIFaW0qg6TP92PxdlHcLXumre7A6DRCCRuYAbwj/8d83Y3mKFyPVY8/PDD+OOPPzB37lyYTCYkJycjOzu7SdC8J3hzy68ePydBEOon/6y8Wc+EYzgA7+0o8nY3CBcs/+EkerQz+MRWoobjOPKfyERlZSUMBgMqKircjt8atGgbzlI2IkEQVmjgW+reBKE0sYZg7Jw51O0geTnnb0fQNqJK6Wps4e0ueIwgSgQgFOZWN8p5TBgUrwopAIAMLWtCdVoaOwgUV9RgX1Gpt7shCBlbKuWth/t4uwseY/DN0d7uAuHnSCnAHhUWhHcf6Y2/jrwF8+5OVKBXhDtU15llK2dG+DakIE9IpkVwIHq2ld+VqUb2Fam/rhXh2/SNF1eAPSosCHtmp2NEzziFekQQ7KR1VH+hZW9CCvKEW2zIGqQqgytrSGc82KctU9vw4ECMu60DWuiFczAu16gj24fwTyJCg/D4bY1bgaybTqVV9Zbitg1mDrP+e0i5DhKEC54anID/69vB291QJRr4joI8ZSOqnA1Zg3Cl5hqmfX4Ae4suobJGfPqvXEG11XXX8O/9vwufTwNU1lzDx7tPy3BWgnCP1+/vAV2gFvPuTsTkT/cz/x2/NbHnt0sor65XqntEMyOmpQ4DO0ejsuYaCs9X4nxFTZPxOUwfgCm3d8ZfBnWELlCL3JOXvNJXX4AU5AnZaBEciFWP98XL9yRJ+nu5ohrW559nO5/CYRQRId4XGXSFjoJ2VYExXI+VVgrTmUmxWPFoH+b4LX5rojlMdBp6ZBVHA2DqsC7YPTsdw28x4qfTZTjnwNACgOraBnRsHQbd9UoB/RKiPJqkwbqD0SpMh/ED4pXtjBMiQ4NIQZ5QBqPBO7USNWiMYSmtqvPK+Xn+nNYBayf0x/Kx6k4emDKki7e74BaeXiRGhemYt/dYuDPJiLUT+mPXrGFNBuLMpFi8cneSy/M13Zrw/yBsjgMe69/e293wGKG6AETYKcMr/dxzAJZuPY7F2Ucw6dP9gt7Sl78pRIO58dkL0Go8lqQRERqEAV3YkpZeGtkd6V5ScS/zMW8zGVs+RL+EKBjD9R4/LwcgNUFcgLES3JkUi7ROrdC/Yyvm0hVjU9thypBOyBrSGVOHdYExXPnVYXyr0CYDudrJGtIJS0cnY87I7jB70LbQaoCX72msPyfXXJd/thz9EqIcbi1kFxQja90Bl+YTB9utibSOzSNbNkCrkZS16YtU1zWgvLoe09K7YOnoZKyd0B/Lxii/iOMArPpRWCiVg/ckDV6/vwfzOHmm9Cr6JUR5ZbdBA1uDVO2QseVDBGg1mHuXd1LQvy0weeW8PFoNkHJdKymn0MRcuqJfQis8n9ENz2V0xdThN2PXrKH4bHwqBnRSznic83WBR2N8Yg3BeGpwQpNtBjED4IDOrXFv8k2IbulZY97MAdEt9FjxaB8YZdomcTZJNZg5zN9wWPDvI0KDbGqu9e/UCqE6/69L980vxXjlbmmhCr6IBsC6n87irp5xSOvUCiN6xuLBPjcpfl4xtgEfN9hg5vDyN4UK9aiRWEOwZduddWG/7qczAIBxXthK9KZBKgUKkPcxjl+44u0ueAUzB+SdLkO/hChRgw4fd9Ng5rCvqBQ5hSaszz+v6JZopZvZlVlDOqGy5hr+mSucYJA1pBOmDe+KAK0GL2R2x56Tl5D720UAGoQHB+Jv3x4VPEarMJ1ly8wbKdQXLtfg3uSbMDzRiNW7irBg0xFZjmnPvqJSmCprBf+2vLoe+4pKkWZlkOsCtaj283qEl6rq0KqlHhMGxWPVj6e83R3F4Sfr1buK8MSABARoNVgwqgf+vf+ct7tmgX8f9xWVorhCXi0pQ0gg3h2bgotXatGmZbCNNzhAq8GYfu2xZMtxl8fgjZ2soV3w0a4iVFz1fGa5L2hsAWRs+RTZBcWCD78/s+vEHzBzHPOgw8fdZBcUY/6GQpgqfeOlHNC5NfYVsQVlD+jc2jJA5hSa8PI3haIH5QX3JlmOwQfiyj2wu4KfUAK0GjwxIAEf7Cxy+/yOjEYxg7J122XbjjebbMQLl2uQ0iGKydgK0wfgkX7tmbbF5CZMp0VVnVmWYy3YdARvbf0VD/Zpi8sSsr2VIiIkyLIIUsKgWPRATwzo7HyLPD46jOk4Fy7XIECrQb+EKOQUXpCre8yculjt8XNKgbYRfQRPuJHVzrLtJzHlM/bU/Xl3JyKn0IRJn+73uKHVMlj8OoYPzE7pEIm1+84ItrcO4s4uKMbkT/eLNlKeGpyAET1vBJF7MhDXkUaOu+d3pbsjxmvHt21uC5zoMD3zOFNV24APdkoztG7rKF0XaUy/ttAFyLute7mmAR/vPs0kbeMp0ru3sSyC5PY4h+kCbLbKHXHqYhXTsdq0DEbdNTO2HPG8oQU0bmX6QtwWGVs+ghJuZHe4MynGK+ctv8rmYZiWfjOGJxplFaOMCgvC47cJiwvGGoKZU6d5+FDueXcnIu90GdN21+i+7RGg1VgMcTHDDV+KZvaIpobN8EQjwhSOUbL+vvaB7JlJsZLSyV0dE2BPMOGNtea0wOGNVGggapyRKvMSyiB27Iy1+35HGeM44MsM6NLa8v+8x1muJJKqugbs+c2597zBzIla8H2y+5Tikj/O8JW4LTK2fASxbuTbFa43+Oe0BKx8tI9qCvRaYwzXI2toZ8lilPYDmub6z9/u64GX70nCU4MTXP7tvLsTRWcjGg3BFs0Y1nsdHx0KQJoh/s7oPk5L0SzbdhxVMscn2V9T6+/rCCnp5ELHDNBqMP965qMreGNNbQscpbA2Ui9eETby5cBbXhBfwjoj0NrjK5fB5Uo/jjW+kV/w/XTKu8aOL4SIUMyWj8DqRg7TBWDi4I6obzDj+18vyt4PDRonNT6YcniiEfuKSnHhcg2Ol1zBsu0nRB9TqxGXoeOqbwAw/55bEKDVYNcJ8d9/WvrNWPfTGZtJ1mgIxry7Ey2T+OwRiejVNhIvfV1gE2gfe70dANFbT5zVspD1XvPtpMRzXKxyPJA2mDl8vOuU6OMJwX+78QPikZ5odCrNwCM2dmzOyO6WIGdXZCbFYuWjfTDrv4eaGOKRoUFYeH8Py332lcBbFrQaYMKgBPRuH9kkrs/6+W4OAq6+gFYDlNkl8fCivFLiMh3jfNAVu+DzdrZuqYcWCe5AxpaPwDL5hOkC0DI4SLEYE0fbNAFajSVrK/fkJWZja1p6F1RcrcdHu05JNrQiQoJsthXtjaKfGYPMgRtGZNbQzsga2tliQNpn6fCM6BmLjCRjk3YAMHDRNtHfpaSyFpM/3Y8Vj/bB8EQjYg3BMDlRlwYa5QncySB09jf7ikqZt2rFogGwucCEF0cKl9fgV/KTGMvrRLfUM5fsyEyKxfBEo03mJq/fZn0MXyhuy8Jj/dtjzl23WNTIrRdI0WF6QANcvFKLXccvoq5ePQHizRkzB0xZsx8rtLaeWv7Z5e/fqYvV+HhXkaR31pV+nNgF3wO92zJXGFGCqDCd187NChlbPkKAVoN7esXivR3OA1Kr6hqYtn+k1kq0N2bsKXPiLbFGqwGWjemDjCSjJKPEmuWP9IFWq3FoFGUXFGPf6XLmY3EARlw3nvolRNmk/TvD2tDkyT15SdKqk8MNkb7hiUZBQ6O8uh45hSaLJo6QccZj7Zl0hJLeHGtdHJbrm5kUi2npXZgWD2INowCtBgO6RLtUyvZGZqac8Pd6/j1JNkYk/9xmFxTjuX//4rPfz9/hcGM8cHT/eLKGdrYsHPYVlWLfqTLBY7fQB6K/i3dQaEyxH0du6xKNUF2A1+RRvFVdRQwUs+UjNJg5bPilWJZjGQ3BmJbOVlJGA2BYt9ZYO6E/ds4c6tTQajBzTPpIWbd3tniEpA7yfDBv/06tkNapFe5NvglpnW54JVjFK62PBwAf7jqFMav2YOCibcgukHat3TFWrI2R4YlGwbivF786hK8OnMO+olLMGSkczyEUQA54xpsj5hplDXWt+u8q+9BdPJmZqRTO7rXU7FXCs7AEf/MLh2nDu+JMKZsMwuIHerr0BLuKEXO2w/GP/+vFdG65Uer9lxsytnwEOYN1OY5DlzYtBCfz4CAtCl/JxIdP9LMxZtzp39vbT2Dgom3YUihNkZ7FYGAN7uSxX7mZKmow+dP9kgwuOYyVC5cbB1ih4P7SqnpM+zwfY1btwYJNhZg4OMGlCrtQADngmYK3Yq5RY1B7oiVJwRqWZ8FdeO+ar6HVNHp+Hd1rKdmrhPdgXZywjnu920UgMkwnKJfAx4jZjymuxhFPlynjE5KUev/lhLYRfQQ5t3dKKmvx9JoDgun9IUEBljgPIcT0z1RRgw8lBmFHhenw2n1JLg0Gd6+V/ZaetcdMKJaLlxcQY+zZ06ZlsOjvUFxRg/d2FGHcbR3QNjIUUS30aNPiRjxOm5aN+l15p8vwdf45p/0P0GowZ2R3PL3mgOT+O0NoC9MZzgKDhba15YJV3FFNmDkg0kkcS3PJsvQXWBcnrGPGgbPlGLNqD2INwZgzsjsiw/ROxzT7GDFn4wbvKfWkAW8M12P+Pbco/v7LBRlbPkJ0C/lq1vEvhFB8V5mDsiXOEOOt4M+v1TTq9Ih5QV8a2V3w5ZLDu2QfX5RdUNxkso91MNmzlrlwBh/47koDxxUf7z7tsG/ZBcX40xvbBfsPAJFh4p+1x9M64BOG8kJSV6Esg769Mcwbl64mCRZ8NVDefvLlr8+3ErfICc/TQh/IvDgR+5wWV9Q0WVQ5G9NczQHe8pR6S9dLKmRs+QDZBcWY+Z+DXjn3/w4XMxlbUoKJeS+2mIB9lkBIObxLPBcu1zhdtfHbjfYudXc8IXzge0u9e+54674BENV/KZ7BzKRYpHVq5TQt3ZlhJwZXg74jY9heUkRqH3w1UN568nV0fQj1ExTAvjgQkyjjDGdjgiu85SktuVyLSZ/utxTPVjsUs6VysguKMenT/V4p8AkA/8w9jc0HhVfCfLakWJ4cEO8yzohHTCA0q3glC3z5EkeDF/+7l78ptIl/cMcTwm9fXnBTN4bvzfwNhzF/g+v+z99wGLtOXMTX+eeQe/ISokLZ06it70tmUix2zhyKtRP6Y8n/9cKckd2x5OFkweQKd3EW7G0fkiI1Fo/fWvUV7N8VCob3XfjdBRbkED51Nqa5Qmk9OqFQlln/PeQT5XrIs6VixGbVKYGZA55esx8rta5XD1KzJQ0hOuycORT7ikqRU2jCR7tONfF08f8e3bcdNh48z7Ql5Eq8MkwXILiFyscXCZUvcSRn4I4nhD+eHCJ9HCDo3ePbjP1gr+V3kSHihgVnumueQMwWhrNYPBakbK16AkfvCnDjnlAwvO8jxpjJTIrF8kf6NBFcFoNYiRalt9nrrrkuOF5eXY89Jy+5lHFRA2RsqRixWXVKIjRBSXUlv7XlV3Q1trBsQ/VLiGqy3WG4nuFiHQfFsiXkSLwyNSEKz//7IJMemZjyJdYDIr/CdCdgNCpM5/aWgFTKGL2oGjjPePMUYp87sRMJjxrV5KcO64J/7jltM6naJw34azD8HYkx+K6wxNvd8AhijJnsgmIs2FQo2dCyxvqZd5UcxLJ9KVXbkZXc3y6q3tiibUQVo6YBXkjvxZ2+Wrusrbeilo5OxrT0m1FRXd/EO8W6JcRr0DyX0Q3PZXRFYICWqY7W1PSbkZkUK1pJmYfPoLOXUWgZzFbWwmgIkb0WmtxwcJ7x5imkPne7Tvxh2TZl2YJQW5B8RGgQ1v101mZSjQoLwhy7BBI1jSFyYQzX47H+wgXh/QExGlJybxfzz3x2QTEGLtqGMav24Nl1+U20CFm2L/8yKF6WPjlHraPkDcizpWLUNsC7Gril9tWRp4Hfimowcxi4aJvTeCMpW0Jia36JVVK2xlEGXUqHSPzpje1MxwvQarDi0T6Yv+Gwajyc9jjLeHM3A5AVqc/dsu0nLf/fMjgAKe0jMahLazyWFu8wRkSO4GM5aVx82C5AyqrqMWXNAazQaiwGl9rGEDmouWZG3mllCx93iArB6dKrzO0jQoMQHBgge0HkOQylrQB5MwKtxyDW5CB+cekobCMiNAgt3Ez4EcKToQtSIc+WiuGz6uTijsQYt/7+4uVap14AfjKSOq06MoKEtkCsDTVWxHqqxCop28MbjrzKvS5QK+F46l21HS+5YvEOCa2AlcDd5w4ALtc04PtfL2LBpiPoNudbLNxc2KQN/xyowdByhqPgZjmuj9oor67HW1vFF7wf3j0GAxgn5cE3txZ17CfS4rFr1o3kkOBAea44q+dYru1i6zEIgOjkoAoHQswV1fWK1esFGgvI9+9IxhbhBnJm1QHAzTEt3fr7BZuOOJ083S1tcupi0zITrF4oMVslQpOPo6xHKUrKrmA9Hr+qlHu1LCfLtp/AmFV7kPJqDiY52MJwR42fBTkysKwxc8B7O4ocGly+gP0CRO7r48vkHCnBrpNs+nXJ7SIRpmfb8geA+gazZWF1X5+2+PuD8pSukXsMzBrSCUtHN2YIv/tI0zAH6zFIzGLXlWdN6QXKwvt7kII84T5iivE6g3cLp3VqhWXbxa8IrXGlw2JxJf/nkOgq9NaB8jysQq5iy7/wwetCmVzWsCopsyJ0PE9kkUkRlXWGs9JC/LFf/OoQhnaLYa5IIAZnCvP2OltiWPVjEWbc0c3SX/5++ArWk6+z6xNrCMY9vWKx4Zdim99HhQWhtErc+ysHESGBKPeSxI09Z0qrRYlm5p8tt6nMcFfyTfj64HnkFF5wqx9SY0adMaBza5stN75OraMxSIyh541EDDm0+zwJGVs+QNbQLlj142+4UiutojqHRgOif8dWgnEnQhOUUKxUZlIsWgYH2UgJsGJ9zOyCYkHZC0+Xf5Fb1sDV8TwxeEkRlZVKaVU9+i/cir8JlFqSirP4OF5B/pjpMt79/qTwga5j5oB/5Z7C+EEdAfheVp+9p9iVcT/jjm74V+4pnC6tRoeoUESG6jD9y1+YzxUREiR6ceUIrVY9Gy3/2nMK1QwZyzy7Tl6yeM14I2DVn/vitU2FWPVjkaQ+tArTMY9tUmNLXY1BYgw9TydiZA3phGnDu/qER4vHq093fHw8NBqNzc/rr79u0+bgwYMYNGgQgoOD0a5dOyxevLjJcb788kt069YNwcHB6NGjBzZv3mzzOcdxmDt3LmJjYxESEoL09HQcP27rKSotLcXYsWMRHh6OiIgIjB8/HleuXJH/S0sgQKvBQyltZTmO0JbC42kd8Fj/9i6PIxQrxRt1Yl4D62Pe2D5zHhTubhFi+6xHpYU3xeKpwYtVVFYOSqvqFN9StI+P4//dSkLW5OnSGwaLr2X1vbXl1ybX2f768IuaP72xHQs2HcE/c09jwaYjeFXkFuq4AQmy9FkOuQK5cMezV1xRg0mf7sfSLb9i1p3d8eurd2LOyO74c1oH/HVEN8S0ZPPYL7g3iXlscze21BFiQi48nYgxoHNrnzK0ABXEbL3yyisoLi62/DzzzDOWzyorK3HHHXegQ4cOyMvLwxtvvIH58+fj/ffft7TZvXs3xowZg/Hjx+PAgQMYNWoURo0ahYKCAkubxYsX4+2338bKlSuxd+9ehIWFISMjAzU1NwbQsWPH4vDhw8jJycHGjRuxY8cOTJw40TMXgYE7bpFuBPCeqAYz5zReiH9uP959Gv/ac4bpuM4mIHfiREwVV5m2z6TGS1ljPfn0S4jCvqJSZjmABjOH3JOXRMkHiEHOWpiuGJ5otDE6s4Z0VvycYtSp5SJKwvXsEBVq+X9fzOoTus7OpALEGBqtwnTIGtoZKx/tIyrGqTmwZMtxDHh9G7YdLcH4QR3xyr1JmDC4E16+VzgOd3hiG4zoqUwsKCtCSSH8jkmAVmMxzDyBGDkMNeH1bcSWLVvCaDQ6/Oyzzz5DXV0dPvroI+h0Otxyyy3Iz8/HP/7xD4shtHTpUmRmZuL5558HACxYsAA5OTlYtmwZVq5cCY7j8NZbb+Gll17CvffeCwD45z//iZiYGKxfvx6jR4/GkSNHkJ2djZ9++gm33norAOCdd97BiBEj8Pe//x1xcXEeuBKu6ZcQhTB9AKokbCXayytYbynwqu1S5j5XE5CzrTohSqvqmNr//cFesonYsRaZltpeEgrbIvYSE/xWQu7JS27H9blCqqiouxjDxU0EWg3wWFq85d9qk34QQug6yxUT2KudAQHXpSau1psx7fN8N4/oX5gqm8a48tUtpn/xi9Otyi2FF5BdUCwp+cZeyDmtUyvFs/V4w2zSp/sVPQ8A3NMr1ue8WoAKPFuvv/46WrVqhd69e+ONN97AtWs3AiRzc3MxePBg6HQ3tgAyMjJw7NgxlJWVWdqkp6fbHDMjIwO5ubkAgKKiIphMJps2BoMBqamplja5ubmIiIiwGFoAkJ6eDq1Wi717ncce1dbWorKy0uZHKXIKTZIMLWvsPVFmM4f/7j8n+jisdQr5rbrPxqfi6ds7ooWLlS9/TFYPxMUqeXSnnK3unWXRiW0vFbm+nyNcbSvILTfiDE9vy4ldeU8YlGATzO9uti3QaMANT2zj1jGAxvsXEcKmW+TsOssVg7bt6B+WZ/70RXWEXagRey/j8EQjwoNd30OpHuCcQhOe+/cvWLb9JJZtP4GxH+yVJMEilBRivWPiSTb8UuwTtRDt8aqx9f/+3//DunXrsH37djz11FP429/+hhdeeMHyuclkQkyMrTYU/2+TyeSyjfXn1n/nrE2bNraDYGBgIKKioixtHLFw4UIYDAbLT7t27Zi/uxjkyoSyVwQe++Fe0YGtYvf/+Rf/3e+dB/hbH5PVAyHHtg5LurL1YMLS3r6os9RBgfX7RYXpRG/VutpWkFtuxBme3pbjjSUNXG9tazXAU4MTMHtEU8PKWVUAVswckFN4AWE697bbOADjBsQztXV2neU0dqd/8QvqrpnxSe4p2Y7pTziKcW0sxSavhiAg72JQivSDJyiuqMEeRgkPNSG7sTVr1qwmQe/2P0ePHgUATJ8+Hbfffjt69uyJSZMm4c0338Q777yD2lp1qmXbM3v2bFRUVFh+zp49q8h53F2FWnui3C3pIGb/n/Vc1seUooMlFbGiqSzt+aLO7op69kuIQkSo65VvRGgQXr03iWkrKCosCEseZksEyEyKxXjGydwRLYOdRyfIef/E4iympWVwAG6/ORpzRnbH0QV3OjS0rI+xc+ZQt2LbWOpyuiJUF4DJt3d26z2R09itrmvA7P8eRFm1OmQb1Iq1gauEhqDYxaNc5/aG9MPTa5RLtFEK2WO2ZsyYgSeeeMJlm44dOzr8fWpqKq5du4ZTp06ha9euMBqNKCmxLTbK/5uP83LWxvpz/nexsbE2bZKTky1tLlyw1UO5du0aSktLncaTAYBer4der+4tF1ZFYFfMGdkd0S31orSlWGJCIkKCsHxsH/Tv2MpyTKk6WFIQO+BJuQ+udMncRYNGnRwWHbbSqnoYw4OZ46TSE434cNcpSf16KKUtPr7+t0rePynIoZcWoNVgQOdoRWPbXFFd14C802VuvSf8okZogkxuZ0D+2QrBPn2dLz4coblhbeCeulgl+m+EYF08rt5VhCcGJAg+82qWfqi4Wo9Jn+7HSgXGVaWQ3bPVunVrdOvWzeWPdQyWNfn5+dBqtZYtvbS0NOzYsQP19Te2unJyctC1a1dERkZa2mzdutXmODk5OUhLSwMAJCQkwGg02rSprKzE3r17LW3S0tJQXl6OvLw8S5tt27bBbDYjNTVVhqviHu6sQiNCg5gVge3hV8dPDEiwSRdngeVc5VfrodVoHGp1yZlV4wyxgoFS7oOUFSXQeP2cCYXylFXXY19RKeKjw5iOKafSviuGJxo9cv+k4kgCQSwpHSK9qsieU2hy6z0J0GowZ2R3wfMcL7nM1J9rZqZmzRJ7L2ODmcPafcIZ32I9wKzvt6tKINaoWfqBZ/Z/D/lM/JbXshFzc3Oxd+9eDBkyBC1btkRubi6mTZuGRx991GJIPfLII3j55Zcxfvx4zJw5EwUFBVi6dCmWLFliOc6zzz6LP/3pT3jzzTcxcuRIrFu3Dj///LNFHkKj0WDq1Kl49dVX0aVLFyQkJGDOnDmIi4vDqFGjAADdu3dHZmYmJkyYgJUrV6K+vh5ZWVkYPXq0ajIRpWZClVlN2GImWyleCOsixMdL2IJld534w6FnQW7FdkeIFQKUeh+kZOCJ8brJrTINuPYwOsM+w1Hp++cu7hTNXvH9Ca9mJX6dfx5/HZno1nsSGSbsla+qIyvKHRyNo43xWsKhMqP7thf1voh5v1k87mJ2GbyVrVtWXY89v13CgM7yZKYrideMLb1ej3Xr1mH+/Pmora1FQkICpk2bhunTp1vaGAwGfPfdd5gyZQpSUlIQHR2NuXPn2uhf3XbbbVizZg1eeuklvPjii+jSpQvWr1+PpKQkS5sXXngBVVVVmDhxIsrLyzFw4EBkZ2cjOPjGw/nZZ58hKysLw4YNg1arxQMPPIC3337bMxdDAOuHXizWau9iXkYhNXV7HMkhsLBs+0n8Z/85h+eSW7HdHrFblgFaDe7pFYv3dkhThBZj7IoxoKSqRwshRr7D2fXypLyDGNyR79h8sFjRwrosXKqqsxjvUq+zN4VaNddLRfkKrhYc/LM/cXBCk9JHjsZR1useHx0q3MgKPpOYxZATqgTCw1ptQ8riTC5yT/qGsaXhOF965NVNZWUlDAYDKioqEB4eLvvxX9t0GKt+PCXpb9dO6I9+CVEYuGiby9WHozgqIfhAeKkPEn8WubeYWD0XrBOvu99z7YT+zJNig5lzea94A2rnzKEWJXDeGHfUfvyAeKQnGiV5l+qumW3KubQJD8bfNh9RVmdMQZzdR5bncGP+eWStO6Bo/1hZOjoZ9ybfJPnvc09ewphVe2TsERtSay/+Oa0DOkSFYsGmIwr0yjEt9IFY/EBPLNjkfMFh/eyzjDms1511vLA+54+/XsS/9//O9uVEnMedsTQ4SIuaeuU8pFlDOuG5jG5uH0fp+dvroqYEGw1mDv+RoInFc+FyDZMn5/UHeohaJcghjsi6yhKDGM8Fy1aMO99TimfJlTfTkRdJqBjzh7tO4cNdp0QbRc6u45yR3REZplftFqEzhDK2XD2HCzcXSvZqKoG7cTIsHtGYcD0AjUuZAjFoANyXfJOk5Is7r2crf7CzyGOZb1dqryEyTIedM4daxofoMD2gAS5eqW3y7LN4GeX0REvdUbCGxdPGbxXy12BfUSlz+Edx+VVRtTbFktZR/V4tgIwtn2FfUalb9br4gVlqEWZX/ZJj4JNTWdyZ58JVnILQICn1e7qbgWcIDWoSKB8RGoSF9/dwaTQ6qwwgJjvS1XWcsuYAVjzaxy3PijcQI/dh/TxsPnheVYZWREgQzByHBjMn2cgV2vrhAIzp1x5d2rTElDXiPbpRYTqbeoe8oW8I0YkytuxjAT2lVM7DL1Tl2hKXK+PaXU87jyujnfdo5RSasD7/vMP7KTSW5iqoiRURGoT+Kg1VsIeMLR9BanyFo1WSnMHncsd9uHs8dzwXro6568RFSf2RasS6GkjLqutxzHQZtdfMDlfW/RKiMP2LfIfHZb0GSlxHNSBF36jBzOGlrwtctPY85VfrMfaDvW5v3wrF5S3ZchxhugAkt4vA6dJqpmLR/Jjzw/NDkHe6rMkY02DmmIOpnXlxVz7aB/9v7QHUNSgfBaNEph1/3edvKLTxGrKOF3LsKAh50IS8ZqwLN1aZESm8fn8Pnxl/vF6uh2DDnRfe0SpJjvR3d/ulxPHECpUKwavtS9FUigoLwg/PDxE9EbIMpEu2HHcqnCrHNZD7OqoF1ufr4uVaSxWAPb9dcsurrCRylInihVqnpd/s8POqugYcOFuO0qo6hOkDMH5APKal3+xQjd/aONIFah2OMWIK1TuTschMisWHj/cV90UloKQA74EzZSix254trqjBN7+cE5QzkEPoGnDuQWMRpGaVtbGu3iCFqcO6NCkhZgzX+5TGFkCeLZ9ByupAqwGWjVH2gZQr5Vdqxpw9ciozu+umL62qR97pMtHbD2IHUvsVphzXQAmFa0/iLKCX5XnVaGAThM1ah9AbyOllXPeTsPZTVW0DPtx1Cisf7cMUjuDsPjgNZwjXY0y/9oiPDhP0uKe6WVw5IjQID/Rpiw93Ot4e1kA5AV5X8X+bDpVgy5FsLB2d7HTsdve9c+VBE+M1Yw3/4O/3jC9/EVXjt4U+EM8M64JnhnVRtYwMC2Rs+QjW+/ysk7+ZAyLDHAvIKtEvqSm/zlZZUnSQ5NKcksNND0gbFMX+jf2EK8c1UEK7y1MIJUcIxfzY52eLrR/qaeSIdxRr4L/8TSF2zhzqMhxB6D64G86Qd7pM3Je04+Fb22LDL449gkpm19ZdM2PVj67j/2qvmV0qpIt97yJDA/F4WjwSWrewXGegMZ7K/tpL8ZqxjlliDC0A+L9b21qeB7XKyLBCxpYPIUb3iIf1JXBH4FFKv6xxtMqSqoMkV6aPXIH/rIOi9fW/eFl8bVDrCVeOa6CUdpfSsCRHDE80IsJB4oGv4463Q+zfWht3jiZB1iQVdwLP3fXuuEp4qK5Trs7jv3KbJq44w5nHUuyOQnn1NSzdegIrHu2DtE6tXI6vtRLKAbAsXudvEF+oenii83J5vgbFbPkYfHwFS6kNgG2y5+OSxqzaI7mAMt+vZ4d1Yf6biJAgfPaX1CaFkd2pXO8qHkRMpo+7A7l9iQ5X2F//BZuOQKqH3Frig++Hfb8A4Wsg13X0JKyFePf8dkkRQ0urEY5BUhJ3vIxS/tbZOyJ3QWRnKOlVrbh6ze1YOGecLq1mbussLlJM3Btge903H3Q9vrLWbeTPzTLOLdt2XLR8iLcK1isFGVs+SIBWgycGJCAi1HUsSURokODD6o5h44gvfj7L1E6DG5perHpWrAO1HLUVxQzk7hgjzq6/1HnIXuKD5Ro0mDnknrxkCQjnr62nalTKBWtQ/6d7Tity/hE9Gq+Hpw0uMYa9M3hPiRicvSOeSq7gFdOVgoM8RqE97SJDRLV3ZtQ6ez+dwV/3l74ucDm+rt13BsZw4bqorONcdoG0iguj+7bDxoPnbcYkX4a2Ef0YoZdF7vR+1q23VmE6vHZfksPJWqoOkj3uxoOwbqPNGZnYRF1azvRtXpRUCKkSH0rH1XgSVm/ktwUmyeeYOqwL/rnntEMJhI0HixGqC0B1nbi4FEdEhgbZ1DV1hlxeRrH6Va6MO08lVwRoNRjTr72ipZPk0P6zD9G4OaalqL93tfDLTIqF2Qy89HUBkywHAJftOACmylpMS78Zb2351WUcrtA412DmsOfkJcz6zyGmflkTERpkc199WUiZh4wtH2VfUangVkhZdb3LgUIuw4aHdfB8aWR3t7NsWFWPpQ6SrMKDmUmxyEiSZoywGKdmDpgzsjuiW+px6mI13tryK+CiP84kPhzhibgaR7gTH+gKpYP1Yw3BlsyoZdtOYMn1e2GNHIZWrCEYD9/aDm9tFTYiolwsXMTC61fN+u8hwbHFVTILa8yhHPcrPjrM7WMI4Y5R6GgxYwhhn3aFPJbZBcWSBGeFiI8OdRiHGxUWhPuSbxIs/eWusr3981dcUYOn19iWyfKlEmEAGVs+ixrT+1kHT6PBuRtdTVlwYoqwKlkIOLql3qLU3tXYQhb1f2+JlrpTAFqIsirxiQViGN23vUWUk0UmQSpigpRdLVykwHsyl207gfd2nGxiPEY6qF7g6J668sgKJVc4MsaBxsWJqeIqSqvqENVCD2N4MKJbKLeNyCN1rHG2mKm4yh5878pjKTZjWgMgkrEuZZuWwUjr1EqSV1suZXshxFTDUANkbPkoakzv98csOCW30aRcf7n6I7dXkwUpZZRYaTBzihcojo8OBSBfpqo91rp4rCVOXC1cpBKg1eDZ9C7IGtoZe05eQu5vFwE0LijsC9Q7u6euDC3AtZimveHGx6Y68rYZw/WICA1CRXW9IpO71Fg4OaRjhnRt7fJ9EPMc8lf61XuTsGDTEebxVexCUi7JHBZ8rZIFGVs+ihoNGzlqfslVN0xO5N5G45F6/eXoj6dFS5X2pCllAFnDG71KCbm+/XBvjOjZOLmqYdERoNVgQJdoDOjiuNCvlJhDV15YZ4abqy1NU6Wy3kypY40cz+PB3ytc1r4U8xxaX3etVqPY+OqJ99AaJRaFSkHZiD6K2tL7+Yy22mtmTE2/GTHh0jPYfC0LTirelFfw9Hat0hlqrBNPREiQ6GxB+2w/Jbaww3QBCAy80TNfkN4QE3O4dHQy1k7o30TmhUcJj0isIRh395Su0zTutg6Sxxo5DPJLVXUu3wfW53DOyO42113J8dVbFSXUWsnCGvJs+TCsMUVKH8OR698Yrse09C5MZTec9ctXsuDcQY7rLwVPe06U9qSxxu48cVs8lm49zlztwJFhw0sOyOlVqa5rwKRP99u8M8MTjV55NliREnPoDLk9ItPSuyBraBdsPHge3xyUln16xy3Sr69cBrmra8z6Dj8xIKHJuKnU+OrO944ICQQ0GklbwmqsZGEPGVs+TIOZQ0t9EO7rHYfz5TW4KTIEt3WKbhJXIYQ7L54z139JZS3e2nLcolgsBaW279SGNwxLT2/XKu5JYxyd+8ZHOTRgYg3BuKdXLDb8Uixo2CghOcB33z7dfd7didg5c6gqFx1y3lM5PRMaAOt+OousoV0kP09aDVDGKKXgCLlqxrrqv7vvsBLjK1PtUdj2NSIkCOMGxGPy7Z2x4vuTDrN8naHWShaOIGPLR8kuKHaYov3Z3jN43S5byBGOMn7EvnjeymjzR7xhWHrSq6a0J+0iYybixapa3Jt8k1Pj9oXM7kyGjSckB9SebSXnPZXTM2G9JS01Q9XMAVPW7McKrbRrL2QIcYDLwH7Wa+ctz7gzWAzA5Y/0bqKXlVNowp/e2C7Ku6mW7XRWyNjyQbILip2KD5ZX17ssYMr/vRzp997IaCPkxVNeNaU9aWK9LM6MW1aj1xPbFmpfsMh5T+XyBFljqriKxf87JvnveQV5qddeyBAC4HQc58B+7dQWciHWABSSipiW3gVd2rSULB6tFsjY8jEaC3oeFmznbJBw9mAXS1hFsyt2N5b8Ucv2B2GLp7xqSq7CPR2DpoRx4Ai1L1jkuqeuDDep7D9T5nYcmLvX3pUhJGfdRbWFXLAagEKJEfyW8M6ZQyWLR6sFMrZ8jH1FpUyBuY4GCaEHW+xKjnV1/8/c0/hn7mmfU/wl5EepVbinY9CUMA5coeZsK7nuqTPDTWoJpH/tkUd4Vo6yQvaGED8WO0PNHk1WWAxAsbsjajIoxULSDz6GmBffvi1Lxo+Y9Ht+dc86FPDeMzlXdISyOCtS7Q78IHxv8k1I6yQumcMVnpYMcXa+WEMwnhqcAA3kK0qt9mwrue5pZlIsds4cirUT+mPp6GR8Nj4V4cFBMvdWHNFh8qvUe6pYt9rxtN6fNyHPlo8hZtC1b2uquMr0d6ztpKzu3Y2DIDyHkqV1lMLT8SvOzgcAobogfLyrCOVXbySxOMt6dIYvZVvJhbVHJPfkJZgqvTvRzvjyF8y/R95nvjkZGa5QU3k2pSFjy8dg1fhxVGZCjqrw9jhz/btCzTEovoJSxZx5lCytozSejl+xP5/DkjPX09uzhnZpkvUopcB4c0ENxkZJpfzPvLeNDKXHD9ZzqqFSgqcgY8vHCNBqMP+eW5xmsfA4GqCjGIUfWdvxWK/uNx06j08ZYiVYvWeewBsDjzso7XEiSQ/pOC05c7UeS7YcR5c2LTCiZ5zFQOOfvXED4rE+/7zNQsfXsq2UQA0eDSWeeW8aGd7wWLs6p9rKsykFGVs+SGZSLFY+2sehzlZkaBAWOtHZMoazDVys7azhJ4/C8xVM7cV4z5RE7Vtl9oZgWVUdpqxR1uNEkh7CODLQAQiWnMlaewDLoMGInrEOn72osCDcl3wT0hONqjf6PQFvlHiy3p4j5H7mvVUD1hsea5ZzqkkrTCnI2PJReG/SnpOXkPvbRQCNxo4r9XiWgctZlXtW749S3jMlUPtWmaPJWKtxHBsn5+qb4klc48xAH923HVOtwKfX7MdTvyfg/R1FTe5laVU9Ptx1CikdIpu9oQXcMEqEPPnucFePGHSIboHl208KtpXzmfe0IKk3PNas59w5c6hN7GN0mB7QABev1CL35CW/WHiQseXDBGg1GNAlGgO6RDO351dTAPtqSoz3R0nvmZyofavMmSHoKhlQrtW3t+NJ1IwrA11M+Z5VPzY1tKyx9oA1d4YnGvFgn7b49/7fFTl+3pkKjEmNZzK25H7mPZnQ4Q2PtRRph+yCYjz3719Uu9sgFZJ+aGaITY/nJxf7F4b3/tjLOPDeM1c48555EjWnXgvpoQnh7upbSNJDA3XcQ08jZKCLQUhBg/eANXeZlOyCYgxctE0xQwtofM/BwWvPvFJSKPZ4w2Mt9pxi5xtfgoytZoi9ls3aCf2xc+bQJoYWy+Ty8jeFNtpLvPfMkcYQ/zs1BDyqeauMRQ/NFe6uvvl7CDi+h4A67qGncfe+SMH+/WpOOJt4leBiVa3XnnkltOwc4Q2PtZhzSplvfAkytpopLKspqd4fT4tLSkHNW2VSDTw5V9++cA89jTcM7+YgbOkId727YokO03vlmec9d2NW7cGz6/IxZtUeDFy0TREPjjc81mLOqebdBjlQzNh67bXXcNtttyE0NBQREREO25w5cwYjR45EaGgo2rRpg+effx7Xrl2zafP999+jT58+0Ov16Ny5M1avXt3kOMuXL0d8fDyCg4ORmpqKffv22XxeU1ODKVOmoFWrVmjRogUeeOABlJSUiO5Lc8Md7w+r98xbqHmrTIqBp8TqW+330NN4K0atOSYiyOVFzLglhq3h9VfGk8+8p7fMvOGxFnNONe82yIFixlZdXR0eeughTJ482eHnDQ0NGDlyJOrq6rB792588sknWL16NebOnWtpU1RUhJEjR2LIkCHIz8/H1KlT8Ze//AX/+9//LG0+//xzTJ8+HfPmzcP+/fvRq1cvZGRk4MKFC5Y206ZNwzfffIMvv/wSP/zwA86fP4/7779fVF+aI+56fzwViyAFNW+VsZRBsu+WUqtvNd9DTyO2PJVcNMdEBLkm1C5tWjK1u3jlhkg06zPvzvaft7bMvOG9Yz2nmncb5EDDcZyintrVq1dj6tSpKC8vt/n9t99+i7vuugvnz59HTEzj6mPlypWYOXMm/vjjD+h0OsycORObNm1CQUGB5e9Gjx6N8vJyZGdnAwBSU1PRt29fLFu2DABgNpvRrl07PPPMM5g1axYqKirQunVrrFmzBg8++CAA4OjRo+jevTtyc3PRv39/pr6wUFlZCYPBgIqKCoSHh7t13dRAg5nDwEXbBIX3ds4cyjQJq1E8VK06W/yqF3CcNbr8kd6IDNOr6lo2B5zdF6WIFfF++RO5Jy9hzKo9kv+eH5v+/lAvjP1gr2D7tRP6i8rAc3fcYP1+YvvFiloU5K3PKfd8Ixal52+vxWzl5uaiR48eFuMGADIyMlBZWYnDhw9b2qSnp9v8XUZGBnJzcwE0es/y8vJs2mi1WqSnp1va5OXlob6+3qZNt27d0L59e0sblr44ora2FpWVlTY//oSc3h9PxiaIQa1bZUKrwRE948jj5AWc3RelGN23fbO8tyzb/BGhQU4TcYDGsal/x1ayhwvIsf3n7S0zb3ishc6p5t0GOfCazpbJZLIxbgBY/m0ymVy2qaysxNWrV1FWVoaGhgaHbY4ePWo5hk6naxI3FhMTI3ge6744YuHChXj55ZdZvq7PIofwntrFQ8XW0vPUqtDTRZUJNuzvS3SYHjO+/AUllY5X5O4QHx0q8xF9AxaF9dfv7wEATscm/h7dmWTER7tOyaLULpc+n79vmUnF00KvnkSUsTVr1iwsWrTIZZsjR46gW7dubnXKV5g9ezamT59u+XdlZSXatWvnxR4pgzuTvtrFQ8Xi6W1HTxdVJtiwvy/z73FuGHBo9MJYl9aKCgtCaZVtqS1HNLfJ1hrWidfR2JRTaMLARdts/k6jAayDZqRM4HIJgzanAsxi8ddFpihja8aMGXjiiSdctunYsSPTsYxGY5OsQT5D0Gg0Wv5rnzVYUlKC8PBwhISEICAgAAEBAQ7bWB+jrq4O5eXlNt4t+zZCfXGEXq+HXu/9sjOeQOqk70919tTuoSO8h5BhYD95pHSIxJ/e2O50sgWap3CsPSwTr/3YJFR9YfyAeMm1J+Xa/gvQajBnZCKeXtO0DJE/bJm5iz8uMkUZW61bt0br1q1lOXFaWhpee+01XLhwAW3atAEA5OTkIDw8HImJiZY2mzdvtvm7nJwcpKWlAQB0Oh1SUlKwdetWjBo1CkBjgPzWrVuRlZUFAEhJSUFQUBC2bt2KBx54AABw7NgxnDlzxnIclr4Q0vB2bIJc+JuHjpAfIcPAfvKwLp3liHt6xdKzBHETr5A+lwbA5gITXhwpzZCRa/svu6AYCzYVOvzMH7bMiKYoFiB/5swZ5Ofn48yZM2hoaEB+fj7y8/Nx5coVAMAdd9yBxMREPPbYY/jll1/wv//9Dy+99BKmTJli8RZNmjQJv/32G1544QUcPXoU7777Lr744gtMmzbNcp7p06dj1apV+OSTT3DkyBFMnjwZVVVVGDduHADAYDBg/PjxmD59OrZv3468vDyMGzcOaWlp6N+/P3NfCHHwadHHSy4ztVf7dom/C+4R8iAm8DgzKRYTByc4/fz9HUVeTyDxNZR+T+XQ5xNSxp8zsjsZWn6IYgHyc+fOxSeffGL5d+/evQEA27dvx+23346AgABs3LgRkydPRlpaGsLCwvD444/jlVdesfxNQkICNm3ahGnTpmHp0qVo27YtPvjgA2RkZFjaPPzww/jjjz8wd+5cmEwmJCcnIzs72ybgfcmSJdBqtXjggQdQW1uLjIwMvPvuu5bPWfpCsOMorskZvhKb4C8eOsIWb8qRNJg5bPjFtTHlTW+pGqVahDBVsr1/poqrko7PErjvavuPxfO2YNMRZCSRV9PfUFxnqznhbzpbLNgPyGVVdZiypmm8hCP4ocQXYp28rYtDyI+3NdbU/Ex5+9pIIbugGC9+dYgp8SAqTIe/3Zck+btIvT5qvufNHaXnb69JPxC+j6MBR6thF3v0pdgEyh7yL9SQ7KBWb6karo1YnPXZGWVVdW59F6kZc2q954TykLFFSEIo48cVWUM6Y0DnaJ/YluBxd/uAUA9qSXZQo9aSWq6NGKQUrZbju0jJmFPjPSc8g9cU5AnfRcrgZk2XmBY+qXrujbpihPyoJdlBjcXQ1XJtxCC1aLU3vktKh8gmdU3t0Woa2xH+BXm2CNFIHdx4fHnV5q+Ce80JtWzlqNFbqpZrIwZ3++LJ75J3ukzQ+2/mGttRzJZ/QcYWIRqpg5O/xDX5o+Bec0LurRx3svZYVNI9mRWo5m0uZ9fB3b548rv4ojFLyAMZW4RopAxOFNdESEEJQ0POZAc5svZceUs9nRWo1kQQV9dheKLRZZ+d4Y3vomZjllAWitkiRCMUawKgSVwCxTURYskuKMbARdswZtUePLsuH2NW7cHARdvcFvrkt+8ANHmGxSwKnIlT8ll7YvrpSAxVzuOL6Ycc10ZOhK5DTqHJaZ+d4a3vosY4PcIzkLFFiEZoQNYAWDamN9ZO6I+lo5OxdkJ/7Jw5lAwtghmlDQ13kx2EsvaAxky3Bpb0XC8c3xVqSgRhvQ7DE40O+xxrCMZTgxMQq4LvAqjTmCU8A4maykhzEzX1ReFDQv00mDkMXLTNaRIGv/2zc+ZQtyclqduUSotTqkH8Ug0K8mKvg7M+q+G7WENjp/ogUVNCtVBmHqEEYuQH3DU0pCY7KB3orIZAajUkgoi9Ds76rIbvYg2Nnc0PMrYIt1DbIEb4PmowNIRQOtCZAqkb8efrQGNn84JitgiCUBW+MMHKGejcYOaQe/ISvs4/h9yTl9Bg5iiQ+jp0HQh/gYwtgiBUhS9MsHJmNDrKuHSVYdecAqkpoJzwF8jYImTH0UpdShuieeIrE6y7WXtCGZcAVJMV6E3UlB1JEFKhbEQZaW7ZiI5gybKhTByCBV95TqRkuonJuARAgdRQR3Yk4b8oPX+TsSUjzd3Y4lfq9g8UPxyueLQPAAi2UdNESngXf51g1SDtIBZ/vRcEAZD0A+EjCIkPagDM33AYgMZlG16gkAZxAvDfjC1fyLi0xle8jAShVihmi5AFFm0kU2UtTJVs+kkE4c/4QsYljzfKBhGEv0HGFiELcq7A1bKaJwil8IWMS8C7ZYMIwp8gY4uQBTlX4GpYzROEkvhKxqUYNX+CIJxDxhYhCywrdWO4HsZw9a/mCcIT+IKkga/FlhGEWqEAeUIW+JX65E/3QwPYbDvwxtX8e24BAJdt1LCaJwhPofYaeb4UW0aIhzJMPQcZW4Rs8Ct1+6wlo13WEksbgmguqDnjkvdYmypqHMZt8Xpg5I32PSjD1LOQzpaMNHedLR6W1RKtqAhCvVi/n6cuVuOtLb8CcOyNVsuWJ8EOiyZic7unpLNF+BwsK3U1r+YJojnjyOMRERoEACivrrf8jrzRvgmLJiLpHcoPGVsEQRAEAOcej4rqenAApqV3QXx0GHmjfRgxGaa0IJYPMrYIgiAIJo/Hup/OYufMoWRk+TCUYeodSPqBkESDmUPuyUv4Ov8cck9eIlFDgvBxSFOreUAZpt6BPFuEaCiLhXAHSo5QJ+TxaB5Qhql3IGOLEIWzmA6+TlpzzGIh2CFDXb2Qx6N5wKKJSHqH8kPbiAQzVCeNcAcqaKxufKVeI+E+vlC9wN8gzxbBDGWxEFKhdHP1w3s8Jn263+HnHIARSY1q97T16/uovXqBv6GYZ+u1117DbbfdhtDQUERERDhso9FomvysW7fOps3333+PPn36QK/Xo3Pnzli9enWT4yxfvhzx8fEIDg5Gamoq9u3bZ/N5TU0NpkyZglatWqFFixZ44IEHUFJSYtPmzJkzGDlyJEJDQ9GmTRs8//zzuHbtmlvXwN+gmA5CKhR87R98uOsUxqzag4GLtpEn0ofhE5w2HjwPALirZxzSOrUiQ0tBFDO26urq8NBDD2Hy5Mku23388ccoLi62/IwaNcryWVFREUaOHIkhQ4YgPz8fU6dOxV/+8hf873//s7T5/PPPMX36dMybNw/79+9Hr169kJGRgQsXLljaTJs2Dd988w2+/PJL/PDDDzh//jzuv/9+y+cNDQ0YOXIk6urqsHv3bnzyySdYvXo15s6dK98F8QMopoOQChnq6of3PrJAW7++S3ZBMQYu2oYxq/bg2XX5ZDx7CMXL9axevRpTp05FeXl505NrNPjqq69sDCxrZs6ciU2bNqGgoMDyu9GjR6O8vBzZ2dkAgNTUVPTt2xfLli0DAJjNZrRr1w7PPPMMZs2ahYqKCrRu3Rpr1qzBgw8+CAA4evQounfvjtzcXPTv3x/ffvst7rrrLpw/fx4xMTEAgJUrV2LmzJn4448/oNPpmL6rv5fraTBzGLhom2AWC+nwEPbknryEMav2CLZbO6E/bUF7CdZ7xEPvu+9BZXqco/T87fUA+SlTpiA6Ohr9+vXDRx99BGvbLzc3F+np6TbtMzIykJubC6DRe5aXl2fTRqvVIj093dImLy8P9fX1Nm26deuG9u3bW9rk5uaiR48eFkOLP09lZSUOHz7stO+1tbWorKy0+fFn+JgOAE2CaCmLpfkgRWONgq/Vj1ivIm39+haU4ORdvBog/8orr2Do0KEIDQ3Fd999h6effhpXrlzB//t//w8AYDKZbAwgAIiJiUFlZSWuXr2KsrIyNDQ0OGxz9OhRyzF0Ol2TuLGYmBiYTCaX5+E/c8bChQvx8ssvi//iPgyfxWKfvk910poHUqUbKN1c/Ujd/qetX9+AEpy8iyhja9asWVi0aJHLNkeOHEG3bt2YjjdnzhzL//fu3RtVVVV44403LMaW2pk9ezamT59u+XdlZSXatWvnxR55BspiaZ64q7FGhrq6ERK7dAbFaPoGFDfpXUQZWzNmzMATTzzhsk3Hjh0ldyY1NRULFixAbW0t9Ho9jEZjk6zBkpIShIeHIyQkBAEBAQgICHDYxmg0AgCMRiPq6upQXl5u492yb2Ofwcgfk2/jCL1eD71eL/n7+jIBWg2tfpoRckk3kKGuXlx5Hx1BSuO+BSU4eRdRMVutW7dGt27dXP6wBpM7Ij8/H5GRkRYDJi0tDVu3brVpk5OTg7S0NACATqdDSkqKTRuz2YytW7da2qSkpCAoKMimzbFjx3DmzBlLm7S0NBw6dMgmgzEnJwfh4eFITEyU/H0Iwl+QU7qBN9TvTb6J0s1VhjOxS3to69f3oLhJ76JYzNaZM2dQWlqKM2fOoKGhAfn5+QCAzp07o0WLFvjmm29QUlKC/v37Izg4GDk5Ofjb3/6G5557znKMSZMmYdmyZXjhhRfw5JNPYtu2bfjiiy+wadMmS5vp06fj8ccfx6233op+/frhrbfeQlVVFcaNGwcAMBgMGD9+PKZPn46oqCiEh4fjmWeeQVpaGvr37w8AuOOOO5CYmIjHHnsMixcvhslkwksvvYQpU6Y0W88VQVhDWxDNB3vv46mL1Vi77wxMlbT168tQ3KR3UczYmjt3Lj755BPLv3v37g0A2L59O26//XYEBQVh+fLlmDZtGjiOQ+fOnfGPf/wDEyZMsPxNQkICNm3ahGnTpmHp0qVo27YtPvjgA2RkZFjaPPzww/jjjz8wd+5cmEwmJCcnIzs72ybgfcmSJdBqtXjggQdQW1uLjIwMvPvuu5bPAwICsHHjRkyePBlpaWkICwvD448/jldeeUWpy0MQPgVtQTQv7MMEsoZ2pq1fP4DiJr2H4jpbzQl/19kimi+ksUYQ/kODmSPj2Q6l52+qjUgQhCC0BdG8oMnYv6EEJ89DxhZBEEzQFkTzQKqWGkEQzqFtRBmhbUSiOUBeD//FmZYaz/gB8UhPNNI9J/wOpedvMrZkhIwtgiB8FT4uz5XEBw95ugh/w+9rIxIEQRDeR0hLzRq+akB2QbHCvSII/4CMLYIgCEKURhoVLiYIcZCxRRAEQYjWSBNTNYAgmjuUjUgQBEFILkRNVQN8F0p28RxkbBGqhwYEglAesYWoeahqgG9CEh+ehbIRZYSyEeWHBgSC8CyO3jlHUNUA38WZxAd/F1c82qfZja+UjUg0W/gBwX7Qp0woglCOzKRY7Jw5FGsn9MeTA+IB3JiEeahqgO/SYObw8jeFDj2XlPigHGRsEaqEBgSC8B58OZe5d9+ClY/2gdFgu1VoNAQ3S++HPyAk8UGJD8pAMVuEKhEzIFCNL4JQjsykWAxPNFLcpJ/AmtBAiQ/yQsYWoUpoQCAI9UCFi/0H1oQGSnyQF9pGJFQJDQgEQRDyw0t8OPNLatCYhNQvIcqT3fJ7yNgiVAkNCARBEPLDS3wAlPjgScjYIlQJDQgEQRDKkJkUixWU+OBRSGdLRkhnS35IZ4sgCEIZSDD6BkrP32RsyQgZW8pAAwJBEAShJErP35SNSKgeyoQiCIIgfBmK2SIIgiAIglAQMrYIgiAIgiAUhIwtgiAIgiAIBSFjiyAIgiAIQkHI2CIIgiAIglAQMrYIgiAIgiAUhIwtgiAIgiAIBSFjiyAIgiAIQkHI2CIIgiAIglAQUpAnCIIgnELlsgjCfcjYIgiCIBxCheAJQh4U20Y8deoUxo8fj4SEBISEhKBTp06YN28e6urqbNodPHgQgwYNQnBwMNq1a4fFixc3OdaXX36Jbt26ITg4GD169MDmzZttPuc4DnPnzkVsbCxCQkKQnp6O48eP27QpLS3F2LFjER4ejoiICIwfPx5XrlwR3ReCIIjmQHZBMSZ/ut/G0AIAU0UNJn+6H9kFxV7qGUH4HooZW0ePHoXZbMZ7772Hw4cPY8mSJVi5ciVefPFFS5vKykrccccd6NChA/Ly8vDGG29g/vz5eP/99y1tdu/ejTFjxmD8+PE4cOAARo0ahVGjRqGgoMDSZvHixXj77bexcuVK7N27F2FhYcjIyEBNzY1BYuzYsTh8+DBycnKwceNG7NixAxMnThTVF4IgiOZAg5nDy98UgnPwGf+7l78pRIPZUQuCIOzRcBznsbfljTfewIoVK/Dbb78BAFasWIG//vWvMJlM0Ol0AIBZs2Zh/fr1OHr0KADg4YcfRlVVFTZu3Gg5Tv/+/ZGcnIyVK1eC4zjExcVhxowZeO655wAAFRUViImJwerVqzF69GgcOXIEiYmJ+Omnn3DrrbcCALKzszFixAj8/vvviIuLY+qLEJWVlTAYDKioqEB4eLg8F42gmBGC8DC5Jy9hzKo9gu3WTuiPtE6tPNAjglAWpedvj2YjVlRUICoqyvLv3NxcDB482GLcAEBGRgaOHTuGsrIyS5v09HSb42RkZCA3NxcAUFRUBJPJZNPGYDAgNTXV0iY3NxcREREWQwsA0tPTodVqsXfvXua+2FNbW4vKykqbH0JesguKMXDRNoxZtQfPrsvHmFV7MHDRNtrCIAgFuXC5RriRiHYE0dzxmLF14sQJvPPOO3jqqacsvzOZTIiJibFpx//bZDK5bGP9ufXfOWvTpk0bm88DAwMRFRUleB7rc9izcOFCGAwGy0+7du1cXQJCJBQzQhDeoU3LYFnbEURzR7SxNWvWLGg0Gpc/9ttu586dQ2ZmJh566CFMmDBBts57m9mzZ6OiosLyc/bsWW93yW+gmBGC8B79EqIQa3BtSMUaGrf0CYIQRrT0w4wZM/DEE0+4bNOxY0fL/58/fx5DhgzBbbfd1iTY3Gg0oqSkxOZ3/L+NRqPLNtaf87+LjY21aZOcnGxpc+HCBZtjXLt2DaWlpYLnsT6HPXq9Hnq93uFnhHvsKypt4tGyhgNQXFGDfUWlFDNCEDIToNXgnl6xeG9HkdM29/SKpdhJgmBEtGerdevW6Natm8sfPu7p3LlzuP3225GSkoKPP/4YWq3t6dLS0rBjxw7U19dbfpeTk4OuXbsiMjLS0mbr1q02f5eTk4O0tDQAQEJCAoxGo02byspK7N2719ImLS0N5eXlyMvLs7TZtm0bzGYzUlNTmftCeA6KGSEI79Fg5rDhF9fb9Bt+KSbPMkEwoljMFm9otW/fHn//+9/xxx9/wGQy2cQ/PfLII9DpdBg/fjwOHz6Mzz//HEuXLsX06dMtbZ599llkZ2fjzTffxNGjRzF//nz8/PPPyMrKAgBoNBpMnToVr776KjZs2IBDhw7hz3/+M+Li4jBq1CgAQPfu3ZGZmYkJEyZg37592LVrF7KysjB69GjExcUx94XwHBQzQhDeQ8izDNzwLBMEIYxiCvI5OTk4ceIETpw4gbZt29p8xqtNGAwGfPfdd5gyZQpSUlIQHR2NuXPn2uhf3XbbbVizZg1eeuklvPjii+jSpQvWr1+PpKQkS5sXXngBVVVVmDhxIsrLyzFw4EBkZ2cjOPjGRPzZZ58hKysLw4YNg1arxQMPPIC3337b8jlLXwjPwceMmCpqHMZtaQAYKWaEIBSBPMsEIS8e1dnyd0hnS174bEQANgYXHyWy4tE+VDKEIBSAdLaI5oZf6WwRhBgyk2Kx4tE+MNplRRkNwWRoEYSC8J5lZ+HvGlA2IkGIgQpRE6omMykWwxONpCBPEB4kQKvBvLsTMfnT/dDAsWd53t2J9B4SBCO0jSgjtI1IEIQ/kV1QjJe/KbQJlo81BGPe3YnkWSb8CqXnb/JsEQRBEA4hzzJByAMZWwRBEIRTArQaCoInCDehAHmCIAiCIAgFIWOLIAiCIAhCQcjYIgiCIAiCUBAytgiCIAiCIBSEjC2CIAiCIAgFIWOLIAiCIAhCQcjYIgiCIAiCUBAytgiCIAiCIBSEjC2CIAiCIAgFIWOLIAiCIAhCQcjYIgiCIAiCUBAytgiCIAiCIBSEjC2CIAiCIAgFIWOLIAiCIAhCQcjYIgiCIAiCUJBAb3eAIAiCUDcNZg77ikpx4XIN2rQMRr+EKARoNd7uFkH4DGRsEQRBEE7JLijGy98UoriixvK7WEMw5t2diMykWC/2jCB8B9pGJAiCIBySXVCMyZ/utzG0AMBUUYPJn+5HdkGxl3pGEL4FGVsEQRBEExrMHF7+phCcg8/43738TSEazI5aEARhDRlbBEEQRBP2FZU28WhZwwEorqjBvqJSz3WKIHwUMrYIgiCIJly47NzQktKOIJozZGwRBEEQTWjTMljWdgTRnCFjiyAIgmhCv4QoxBqC4UzgQYPGrMR+CVGe7BZB+CRkbBEEQRBNCNBqMO/uRABoYnDx/553dyLpbREEA2RsEQRBEA7JTIrFikf7wGiw3So0GoKx4tE+pLNFEIyQqClBEAThlMykWAxPNJKCPEG4gWKerVOnTmH8+PFISEhASEgIOnXqhHnz5qGurs6mjUajafKzZ88em2N9+eWX6NatG4KDg9GjRw9s3rzZ5nOO4zB37lzExsYiJCQE6enpOH78uE2b0tJSjB07FuHh4YiIiMD48eNx5coVmzYHDx7EoEGDEBwcjHbt2mHx4sUyXxWCIAjfI0CrQVqnVrg3+SakdWpFhhZBiEQxY+vo0aMwm8147733cPjwYSxZsgQrV67Eiy++2KTtli1bUFxcbPlJSUmxfLZ7926MGTMG48ePx4EDBzBq1CiMGjUKBQUFljaLFy/G22+/jZUrV2Lv3r0ICwtDRkYGampupCSPHTsWhw8fRk5ODjZu3IgdO3Zg4sSJls8rKytxxx13oEOHDsjLy8Mbb7yB+fPn4/3331foChEEQRAE0SzgPMjixYu5hIQEy7+Lioo4ANyBAwec/s3//d//cSNHjrT5XWpqKvfUU09xHMdxZrOZMxqN3BtvvGH5vLy8nNPr9dzatWs5juO4wsJCDgD3008/Wdp8++23nEaj4c6dO8dxHMe9++67XGRkJFdbW2tpM3PmTK5r167M36+iooIDwFVUVDD/DUEQBEEQ3kXp+dujAfIVFRWIimqaJnzPPfegTZs2GDhwIDZs2GDzWW5uLtLT021+l5GRgdzcXABAUVERTCaTTRuDwYDU1FRLm9zcXERERODWW2+1tElPT4dWq8XevXstbQYPHgydTmdznmPHjqGsrMzh96mtrUVlZaXND0EQBEEQhDUeM7ZOnDiBd955B0899ZTldy1atMCbb76JL7/8Eps2bcLAgQMxatQoG4PLZDIhJibG5lgxMTEwmUyWz/nfuWrTpk0bm88DAwMRFRVl08bRMazPYc/ChQthMBgsP+3atWO7GARBEARBNBtEG1uzZs1yGNRu/XP06FGbvzl37hwyMzPx0EMPYcKECZbfR0dHY/r06UhNTUXfvn3x+uuv49FHH8Ubb7zh/jfzALNnz0ZFRYXl5+zZs97uEkEQBEEQKkO09MOMGTPwxBNPuGzTsWNHy/+fP38eQ4YMwW233cYUbJ6amoqcnBzLv41GI0pKSmzalJSUwGg0Wj7nfxcbG2vTJjk52dLmwoULNse4du0aSktLbY7j6DzW57BHr9dDr9cLfieCIAiCIJovoj1brVu3Rrdu3Vz+8HFP586dw+23346UlBR8/PHH0GqFT5efn29jNKWlpWHr1q02bXJycpCWlgYASEhIgNFotGlTWVmJvXv3WtqkpaWhvLwceXl5ljbbtm2D2WxGamqqpc2OHTtQX19vc56uXbsiMjJS7GUiCIIgCIJoRJGwe47jfv/9d65z587csGHDuN9//50rLi62/PCsXr2aW7NmDXfkyBHuyJEj3GuvvcZptVruo48+srTZtWsXFxgYyP3973/njhw5ws2bN48LCgriDh06ZGnz+uuvcxEREdzXX3/NHTx4kLv33nu5hIQE7urVq5Y2mZmZXO/evbm9e/dyO3fu5Lp06cKNGTPG8nl5eTkXExPDPfbYY1xBQQG3bt06LjQ0lHvvvfeYvzNlIxIEQRCE76H0/K2YsfXxxx9zABz+8KxevZrr3r07FxoayoWHh3P9+vXjvvzyyybH+uKLL7ibb76Z0+l03C233MJt2rTJ5nOz2czNmTOHi4mJ4fR6PTds2DDu2LFjNm0uXbrEjRkzhmvRogUXHh7OjRs3jrt8+bJNm19++YUbOHAgp9fruZtuuol7/fXXRX1nMrYIgiAIwvdQev7WcBzHecur5m9UVFQgIiICZ8+eRXh4uLe7QxAEQRAEA5WVlWjXrh3Ky8thMBhkPz7VRpSRy5cvAwBJQBAEQRCED3L58mVFjC3ybMmI2WzG+fPn0bJlS2g08tUO4y1u8ph5Frru3oGuu3eg6+4d6Lp7B/vrznEcLl++jLi4OKZkPrGQZ0tGtFot2rZtq9jxw8PD6WX0AnTdvQNdd+9A19070HX3DtbXXQmPFo9Hy/UQBEEQBEE0N8jYIgiCIAiCUBAytnwAvV6PefPmkVq9h6Hr7h3ounsHuu7ega67d/D0dacAeYIgCIIgCAUhzxZBEARBEISCkLFFEARBEAShIGRsEQRBEARBKAgZWwRBEARBEApCxpbKWb58OeLj4xEcHIzU1FTs27fP213yKXbs2IG7774bcXFx0Gg0WL9+vc3nHMdh7ty5iI2NRUhICNLT03H8+HGbNqWlpRg7dizCw8MRERGB8ePH48qVKzZtDh48iEGDBiE4OBjt2rXD4sWLlf5qqmXhwoXo27cvWrZsiTZt2mDUqFE4duyYTZuamhpMmTIFrVq1QosWLfDAAw+gpKTEps2ZM2cwcuRIhIaGok2bNnj++edx7do1mzbff/89+vTpA71ej86dO2P16tVKfz3VsmLFCvTs2dMi0piWloZvv/3W8jldc8/w+uuvQ6PRYOrUqZbf0bWXn/nz50Oj0dj8dOvWzfK56q65IuWtCVlYt24dp9PpuI8++og7fPgwN2HCBC4iIoIrKSnxdtd8hs2bN3N//etfuf/+978cAO6rr76y+fz111/nDAYDt379eu6XX37h7rnnHi4hIYG7evWqpU1mZibXq1cvbs+ePdyPP/7Ide7cmRszZozl84qKCi4mJoYbO3YsV1BQwK1du5YLCQnh3nvvPU99TVWRkZHBffzxx1xBQQGXn5/PjRgxgmvfvj135coVS5tJkyZx7dq147Zu3cr9/PPPXP/+/bnbbrvN8vm1a9e4pKQkLj09nTtw4AC3efNmLjo6mps9e7alzW+//caFhoZy06dP5woLC7l33nmHCwgI4LKzsz36fdXChg0buE2bNnG//vord+zYMe7FF1/kgoKCuIKCAo7j6Jp7gn379nHx8fFcz549uWeffdbye7r28jNv3jzulltu4YqLiy0/f/zxh+VztV1zMrZUTL9+/bgpU6ZY/t3Q0MDFxcVxCxcu9GKvfBd7Y8tsNnNGo5F74403LL8rLy/n9Ho9t3btWo7jOK6wsJADwP3000+WNt9++y2n0Wi4c+fOcRzHce+++y4XGRnJ1dbWWtrMnDmT69q1q8LfyDe4cOECB4D74YcfOI5rvMZBQUHcl19+aWlz5MgRDgCXm5vLcVyjkazVajmTyWRps2LFCi48PNxynV944QXulltusTnXww8/zGVkZCj9lXyGyMhI7oMPPqBr7gEuX77MdenShcvJyeH+9Kc/WYwtuvbKMG/ePK5Xr14OP1PjNadtRJVSV1eHvLw8pKenW36n1WqRnp6O3NxcL/bMfygqKoLJZLK5xgaDAampqZZrnJubi4iICNx6662WNunp6dBqtdi7d6+lzeDBg6HT6SxtMjIycOzYMZSVlXno26iXiooKAEBUVBQAIC8vD/X19TbXvVu3bmjfvr3Nde/RowdiYmIsbTIyMlBZWYnDhw9b2lgfg29D7wfQ0NCAdevWoaqqCmlpaXTNPcCUKVMwcuTIJteHrr1yHD9+HHFxcejYsSPGjh2LM2fOAFDnNSdjS6VcvHgRDQ0NNg8CAMTExMBkMnmpV/4Ffx1dXWOTyYQ2bdrYfB4YGIioqCibNo6OYX2O5orZbMbUqVMxYMAAJCUlAWi8JjqdDhERETZt7a+70DV11qayshJXr15V4uuonkOHDqFFixbQ6/WYNGkSvvrqKyQmJtI1V5h169Zh//79WLhwYZPP6NorQ2pqKlavXo3s7GysWLECRUVFGDRoEC5fvqzKax4oqjVBEIQIpkyZgoKCAuzcudPbXWkWdO3aFfn5+aioqMC///1vPP744/jhhx+83S2/5uzZs3j22WeRk5OD4OBgb3en2XDnnXda/r9nz55ITU1Fhw4d8MUXXyAkJMSLPXMMebZUSnR0NAICAppkT5SUlMBoNHqpV/4Ffx1dXWOj0YgLFy7YfH7t2jWUlpbatHF0DOtzNEeysrKwceNGbN++HW3btrX83mg0oq6uDuXl5Tbt7a+70DV11iY8PFyVg60n0Ol06Ny5M1JSUrBw4UL06tULS5cupWuuIHl5ebhw4QL69OmDwMBABAYG4ocffsDbb7+NwMBAxMTE0LX3ABEREbj55ptx4sQJVT7vZGypFJ1Oh5SUFGzdutXyO7PZjK1btyItLc2LPfMfEhISYDQaba5xZWUl9u7da7nGaWlpKC8vR15enqXNtm3bYDabkZqaammzY8cO1NfXW9rk5OSga9euiIyM9NC3UQ8cxyErKwtfffUVtm3bhoSEBJvPU1JSEBQUZHPdjx07hjNnzthc90OHDtkYujk5OQgPD0diYqKljfUx+Db0ftzAbDajtraWrrmCDBs2DIcOHUJ+fr7l59Zbb8XYsWMt/0/XXnmuXLmCkydPIjY2Vp3Pu+iQesJjrFu3jtPr9dzq1au5wsJCbuLEiVxERIRN9gThmsuXL3MHDhzgDhw4wAHg/vGPf3AHDhzgTp8+zXFco/RDREQE9/XXX3MHDx7k7r33XofSD7179+b27t3L7dy5k+vSpYuN9EN5eTkXExPDPfbYY1xBQQG3bt06LjQ0tNlKP0yePJkzGAzc999/b5OWXV1dbWkzadIkrn379ty2bdu4n3/+mUtLS+PS0tIsn/Np2XfccQeXn5/PZWdnc61bt3aYlv38889zR44c4ZYvX96sU+FnzZrF/fDDD1xRURF38OBBbtasWZxGo+G+++47juPomnsS62xEjqNrrwQzZszgvv/+e66oqIjbtWsXl56ezkVHR3MXLlzgOE5915yMLZXzzjvvcO3bt+d0Oh3Xr18/bs+ePd7ukk+xfft2DkCTn8cff5zjuEb5hzlz5nAxMTGcXq/nhg0bxh07dszmGJcuXeLGjBnDtWjRggsPD+fGjRvHXb582abNL7/8wg0cOJDT6/XcTTfdxL3++uue+oqqw9H1BsB9/PHHljZXr17lnn76aS4yMpILDQ3l7rvvPq64uNjmOKdOneLuvPNOLiQkhIuOjuZmzJjB1dfX27TZvn07l5yczOl0Oq5jx44252huPPnkk1yHDh04nU7HtW7dmhs2bJjF0OI4uuaexN7YomsvPw8//DAXGxvL6XQ67qabbuIefvhh7sSJE5bP1XbNNRzHceL9YQRBEARBEAQLFLNFEARBEAShIGRsEQRBEARBKAgZWwRBEARBEApCxhZBEARBEISCkLFFEARBEAShIGRsEQRBEARBKAgZWwRBEARBEApCxhZBEARBEISCkLFFEARBEAShIGRsEQRBEARBKAgZWwRBEARBEApCxhZBEARBEISC/H9Tt/RO7ivUYAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, compute the current unit usage, plot its histogram weighted by unit pop\n",
      "unit avg and sd usage are 1.00765 0.12235\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity ...\n",
      "CCB cluster 0 with pop 154316.0 originally had use of 1.0114786922459924\n",
      "CCB cluster 1 with pop 24060.0 originally had use of 0.8496180974756382\n",
      "CCB cluster 2 with pop 88252.0 originally had use of 0.955221549923498\n",
      "CCB cluster 3 with pop 125341.0 originally had use of 0.8549454392701692\n",
      "CCB cluster 4 with pop 160383.0 originally had use of 0.9413095478534921\n"
     ]
    }
   ],
   "source": [
    "#continuing w/option 1 - now we determine 12-wedge-capture (frag) vtd lists for the original HD polygons\n",
    "# FOR MD, THIS IS VERY SLOW.  Slow also for MI\n",
    "print(\"This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\")\n",
    "print(\"We'll later add nearby or jettison faraway contiguous units until we approach the aDP\")\n",
    "print(\"  the HD pops and lists already appropriately include captured corner county clusters\")\n",
    "#see above block for preliminaries\n",
    "nonUnitCounties = list (set([c for c in range(nCounties)]).difference(set(unitCounties)) )\n",
    "areaFrac = [0.5 for u in range(nUnits)]  #default; we will accept unit counties if we capture half their area\n",
    "alter = input(\"enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage\")\n",
    "if alter == 1:\n",
    "    for u in range(nUnits):\n",
    "        if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "            CCBno = int(allUnits[u] )\n",
    "            areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "            print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "        CCBno = int(allUnits[u] )\n",
    "        areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "        #print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "# for c in unitCounties:   #UNCOMMENT TO ENFORCE UNIT COUNTIES TO EQUAL USAGE\n",
    "#    areaFrac[allUnits.index(c+0.5)] = unitCareaFrac[ unitCounties.index(c) ]\n",
    "\n",
    "origHDpop, origHDlist = [0. for t in range(nHDs)], [list() for t in range(nHDs)]\n",
    "\n",
    "startTime = time.time()\n",
    "for jj,t in enumerate(popHDlist):\n",
    "    if jj%500 == 0:\n",
    "        print(\"working on HDno\",t, \"time in sec is now\",int(time.time()-startTime) )\n",
    "    hC = HDcountyNo[t]\n",
    "    #if hC in allFusedCounties:  #using a swelling technique as original HDpoly's look skewy\n",
    "    if STATE != \"MD\" and hC in allFusedCounties:\n",
    "        origHDpop[t], origHDlist[t] = clusterSwell(hdCP[t], CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, aDP)\n",
    "    else:\n",
    "        if hC in unitCounties:\n",
    "            iCP, iClist = countyPop[hC], [allUnits.index(hC+0.5) ] #automatically include the home county if small\n",
    "        else: #must probe in-county list vs HDpoly intersxn\n",
    "            iCP, iClist =  getPolyPop(HDpoly[t], unitCP, unitPop, countyUnitList[hC])\n",
    "        nonCP, nonClist = getNonHCunits(HDpoly[t], hC, allUnits, unitCP, unitPop, allFusedCounties, areaFrac, countyGeom,\n",
    "                                        neighborCountyList, countyUnitList)  #this will miss county clusters; see below\n",
    "        origHDpop[t]  += iCP + nonCP\n",
    "        origHDlist[t] += iClist + nonClist\n",
    "        for j, geo in enumerate(CCBgeom):  #special for corner clusters -- intersection area must clear threshold estbd in a prev block\n",
    "            unitNo = allUnits.index(j+0.25)\n",
    "            if geo.intersection(HDpoly[t]).area >= areaFrac[unitNo] * CCBarea[j]:\n",
    "                origHDpop[t] += unitPop[unitNo]\n",
    "                origHDlist[t].append(unitNo)\n",
    "print(\"all done assigning units to HDs based on original HD polygons\")\n",
    "HDpopExcess = list()\n",
    "for t in popHDlist:\n",
    "    HDpopExcess.append(origHDpop[t] - aDP)\n",
    "print(\"Here is a scatterplot of active HD pop excess vs relative to target\",aDP)\n",
    "plt.scatter(popHDlist, HDpopExcess)\n",
    "plt.axhline(-0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.axhline( 0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, compute the current unit usage, plot its histogram weighted by unit pop\")\n",
    "origUnitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in origHDlist[t]:\n",
    "        origUnitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(origUnitUse,bins=50,weights=unitPop)\n",
    "origUnitUseAvg, origUnitUseSD = getWeightedAvgAndSD(origUnitUse,unitPop)\n",
    "print(\"unit avg and sd usage are\",r5(origUnitUseAvg), r5(origUnitUseSD) )\n",
    "plt.show()\n",
    "print(\"prior to correcting for discontiguity ...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",origUnitUse[u])\n",
    "\n",
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "0fdec9f3-be08-4952-9745-bb0e47e5a04d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for t in popHDlist:\n",
    "    if HDvPop[t] < 0.6 * aDP:\n",
    "        print(t,HDvPop[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70a8d159-68bb-4436-8a07-0ea73d3f5b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#END OF OPTION 1 -- SKIP AHEAD TO AFTER OPTION 2\n",
    "###########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "15507d95-4f47-463d-a46f-380a9195cba6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy() #reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e74fe279-1089-4774-8b1f-2bdc8d30887d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for some states like FL, we move in partial counties before running option 2\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state MD\n",
      "clipPoly 0 intersects [0, 9, 10, 20] counties and a total of 132 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "performing squish no 2 for state MD\n",
      "clipPoly 1 intersects [3, 4, 7, 8, 15, 17, 18, 19, 21, 22] counties and a total of 235 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 2 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "print(\"for some states like FL and MD, we move in partial counties via clipPoly's before running option 2\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(cutLine.buffer(0.02))\n",
    "        plotPoly(altCutLine,0.1)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.002),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4bcdcf95-9358-4921-8d58-22e3b5a4aa95",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#new for MD as FL used the below approach instead; this is the first true squished state\n",
    "#now apply the squish to all impacted vtds.  \"tractGeom\" will retain original vtd shapes\n",
    "for i, vList in enumerate(toSquishUnitsList):\n",
    "    squishedShapes = squishedShapesList[i]\n",
    "    for j, v in enumerate(vList):\n",
    "        vtdGeom[v] = squishedShapes[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "78ad4c0f-eb6e-41bd-9166-0c9bd3c57d9e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the faint(original) and squished shapes for county 22\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = 22\n",
    "print(\"here are the faint(original) and squished shapes for county\",c)\n",
    "for t in countyTractList[c]:\n",
    "    plotPoly(vtdGeom[t])\n",
    "    plotPoly(tractGeom[t],0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "73dbac1b-4bf3-480a-b028-6edf55a5447d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alternate simple approach for Florida Keys - translate all toward county CP\n",
      "I translated 28 vtds toward the center of county 43\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ecc512e1-cb48-4c0b-90f4-b90458ee23f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use shifted shapes (e.g. for MD), else enter 0 for most states to use uncut counties 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 10 is inside the map's convex hull\n",
      "working on confirming HD center 605 is inside the map's convex hull\n",
      "working on confirming HD center 1174 is inside the map's convex hull\n",
      "working on confirming HD center 1691 is inside the map's convex hull\n",
      "working on confirming HD center 2231 is inside the map's convex hull\n",
      "working on confirming HD center 2759 is inside the map's convex hull\n",
      "working on confirming HD center 3353 is inside the map's convex hull\n",
      "working on confirming HD center 3864 is inside the map's convex hull\n",
      "working on confirming HD center 4551 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S \n",
    "#For GA, MA, MD we leave CCB's as individual vtd's, draw HDs for ALL vtds\n",
    "#Can draw for each vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use shifted shapes (e.g. for MD), else enter 0 for most states to use uncut counties\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[0].buffer(0.1)\n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList:\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(0.1))\n",
    "else:\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if t not in allCutVTDs and t not in skipList:  #keep low-pop tracts as VEST may have assigned voters to them\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "650ae996-d3ca-422f-8ab0-3ffa3cf6eb67",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()  #nomenclature equivalency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "54f09a52-c279-4647-9860-0729d1912b4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\n",
      "working on unit-unit distances for unit 10 out of 4805 . Time = 0\n",
      "working on unit-unit distances for unit 504 out of 4805 . Time = 87\n",
      "working on unit-unit distances for unit 974 out of 4805 . Time = 166\n",
      "working on unit-unit distances for unit 1375 out of 4805 . Time = 235\n",
      "working on unit-unit distances for unit 1831 out of 4805 . Time = 307\n",
      "working on unit-unit distances for unit 2231 out of 4805 . Time = 360\n",
      "working on unit-unit distances for unit 2646 out of 4805 . Time = 405\n",
      "working on unit-unit distances for unit 3101 out of 4805 . Time = 440\n",
      "working on unit-unit distances for unit 3553 out of 4805 . Time = 466\n",
      "working on unit-unit distances for unit 4017 out of 4805 . Time = 484\n",
      "working on unit-unit distances for unit 4551 out of 4805 . Time = 493\n",
      "All done; total elapsed  495 seconds for MI with 12 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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U0KFDdfbsWW3ZskUlJSUqKChQbGys5s+fr8zMTPXv319Op1OPP/64XC6XpkyZIkmaMWOGkpOTNW/ePD3zzDPyeDxavny50tPT7b0nCxYs0AsvvKClS5fqkUceUXFxsbZt26b8/Pzg9x4AABgpoABTU1Ojhx56SF9//bViY2M1duxYFRQU6Gc/+5kkad26dYqMjNTs2bNVX18vt9utDRs22OtHRUVp165dWrhwoVwul/r06aO0tDStWrXKbpOUlKT8/HwtXrxY69ev15AhQ7R582a53e4gdRkAAJguoADz0ksvtbo8JiZGubm5ys3NbbHNsGHDtHv37la3M3XqVB05ciSQ0gAAQBjht5AAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAH+15o594a6BABAGxFgAACAcQgw6JaKim8JdQkAgA5EgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYIAAJeytCXQIAQAQYAABgIAIMAAAwDgEG7XZ85KhQlwAACFMEGAAAYBwCDNplzKtjrn8j2bHXvw0AQFgiwAAAAOMQYAAAgHEIMMBl1sy5N9QlAADagAADAACMQ4CBcbh8GwBAgAEAAMYhwAAAAOMQYAAAgHEIMDADN70DAFyGAAMAAIxDgAEAAMYJKMDk5OToxz/+sfr166eBAwdq1qxZOnHihF+bCxcuKD09XQMGDFDfvn01e/ZsVVdX+7U5deqUUlNT1bt3bw0cOFBLlizRpUuX/NqUlJRowoQJcjgcGj58uPLy8trXQwAA0O0EFGBKS0uVnp6uAwcOqLCwUBcvXtSMGTNUV1dnt1m8eLHeeustbd++XaWlpTp9+rTuv/9+e3ljY6NSU1PV0NCg/fv369VXX1VeXp5WrFhhtzl58qRSU1M1bdo0VVRUKCMjQ48++qgKCgqC0GUAwcYdjAF0th6BNN6zZ4/f47y8PA0cOFDl5eW66667VFtbq5deeklbtmzR9OnTJUmvvPKKRo0apQMHDmjKlCl65513dOzYMb377ruKj4/X+PHj9fTTT+vJJ59Udna2oqOjtWnTJiUlJWnNmjWSpFGjRun999/XunXr5Ha7g9R1ACGTHStl14a6CgAGu65zYGprv/8D1L9/f0lSeXm5Ll68qJSUFLvNyJEjNXToUJWVlUmSysrKNGbMGMXHx9tt3G63vF6vjh49are5fBu+Nr5tNKe+vl5er9dvAgAA3VO7A0xTU5MyMjL0k5/8RLfddpskyePxKDo6WnFxcX5t4+Pj5fF47DaXhxffct+y1tp4vV6dP3++2XpycnIUGxtrT4mJie3tGgAA6OLaHWDS09P1ySefaOvWrcGsp92ysrJUW1trT1VVVaEuCQAAdJCAzoHxWbRokXbt2qV9+/ZpyJAh9vyEhAQ1NDTozJkzfnthqqurlZCQYLc5ePCg3/Z8Vyld3ubKK5eqq6vldDrVq1evZmtyOBxyOBzt6Q4AADBMQHtgLMvSokWLtGPHDhUXFyspKclv+cSJE9WzZ08VFRXZ806cOKFTp07J5XJJklwulyorK1VTU2O3KSwslNPpVHJyst3m8m342vi2AQAAwltAASY9PV2vvfaatmzZon79+snj8cjj8djnpcTGxmr+/PnKzMzU3r17VV5erocfflgul0tTpkyRJM2YMUPJycmaN2+e/vjHP6qgoEDLly9Xenq6vQdlwYIF+vLLL7V06VJ9+umn2rBhg7Zt26bFixcHufvBl52dHeoSAADo9gIKMBs3blRtba2mTp2qQYMG2dMbb7xht1m3bp3uvfdezZ49W3fddZcSEhL0H//xH/byqKgo7dq1S1FRUXK5XPr7v/97PfTQQ1q1apXdJikpSfn5+SosLNS4ceO0Zs0abd68mUuoAQCApADPgbEs65ptYmJilJubq9zc3BbbDBs2TLt37251O1OnTtWRI0cCKQ8AAIQJfgsJAAAYhwATBF8tey/UJQAAEFYIMAAAwDgEGAAAYBwCDAAAMA4BppONeXVMqEsAAMB4BBgAXcqaOfeGugQABiDAhNDxkaNCXQIAAEYiwFwnvi0CAND5CDAdKGFvRahLAACgWyLAdIKbl+Vf9zYIQ0AQZMeGugIAQUKACQGuRAIA4PoQYDoT3/4AAAgKAkyIcAUSAADtR4BBs4qKbwl1CQAAtIgAAwAAjEOAAQAAxiHAoENxKAoA0BEIMAAAwDgEGAAAYBwCDAAAMA4BBh0iOzs71CUAALoxAgwAADAOAQYAABiHABNEvsMmXDoMAEDHIsAAAADjEGAA4DqMeXVMqEsAwhIBBgAAGIcAAwAAjEOAAdCtcA8iIDwQYAB0mpuX5Ye6BADdBAEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDCwfbXsvVCXAABAmxBgujh+GBIAgKsRYKA1c+4NdQnhITs21BUAQLdBgAEAAMYhwAAAAOMQYHDdchcUh7oEAECYIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAyCkjo8cFeoSABiIAAMAAIxDgAEAAMYhwAAAAOMQYBB0Xy17L9QlAAC6OQIMAAAwTsABZt++ffrFL36hwYMHKyIiQjt37vRbblmWVqxYoUGDBqlXr15KSUnRZ5995tfm22+/1dy5c+V0OhUXF6f58+fr3Llzfm0+/vhj3XnnnYqJiVFiYqKeeeaZwHsHAAC6pYADTF1dncaNG6fc3Nxmlz/zzDN67rnntGnTJn344Yfq06eP3G63Lly4YLeZO3eujh49qsLCQu3atUv79u3TY489Zi/3er2aMWOGhg0bpvLycv3Lv/yLsrOz9fvf/74dXQQAAN1Nj0BXmDlzpmbOnNnsMsuy9Oyzz2r58uW67777JEn/9m//pvj4eO3cuVMPPPCAjh8/rj179ujQoUO6/fbbJUnPP/+8fv7zn+t3v/udBg8erNdff10NDQ16+eWXFR0drdGjR6uiokJr1671Czowz83L8vWn1amhLgMAYLigngNz8uRJeTwepaSk2PNiY2M1efJklZWVSZLKysoUFxdnhxdJSklJUWRkpD788EO7zV133aXo6Gi7jdvt1okTJ/Tdd981+9z19fXyer1+EwAA6J6CGmA8Ho8kKT4+3m9+fHy8vczj8WjgwIF+y3v06KH+/fv7tWluG5c/x5VycnIUGxtrT4mJidffIQAA0CV1m6uQsrKyVFtba09VVVWhLgkAAHSQoAaYhIQESVJ1dbXf/OrqantZQkKCampq/JZfunRJ3377rV+b5rZx+XNcyeFwyOl0+k0ApOzs7FCX0D1lx4a6AiCsBTXAJCUlKSEhQUVFRfY8r9erDz/8UC6XS5Lkcrl05swZlZeX222Ki4vV1NSkyZMn22327dunixcv2m0KCws1YsQI3XDDDcEsGQAAGCjgAHPu3DlVVFSooqJC0vcn7lZUVOjUqVOKiIhQRkaG/vmf/1n/9V//pcrKSj300EMaPHiwZs2aJUkaNWqU/uZv/ka/+c1vdPDgQX3wwQdatGiRHnjgAQ0ePFiS9Ktf/UrR0dGaP3++jh49qjfeeEPr169XZmZm0DoOdEXcxRgA2ibgy6g/+ugjTZs2zX7sCxVpaWnKy8vT0qVLVVdXp8cee0xnzpzRT3/6U+3Zs0cxMTH2Oq+//roWLVqke+65R5GRkZo9e7aee+45e3lsbKzeeecdpaena+LEibrxxhu1YsUKLqEOES59BgB0NQEHmKlTp8qyrBaXR0REaNWqVVq1alWLbfr3768tW7a0+jxjx47Ve+/xbRRor6+Wvachq+8MdRltlrugWOmbpoe6DACG6DZXIQEAgPBBgAEAAMYhwAAAAOMQYAAAgHEIMGhVwt6KUJcAAMBVCDAAAMA4BBgAAGAcAkwXsmbOvaEuAYY5PnJUqEsAgJAgwAAAAOMQYBAa/JKv0djzAyDUCDAAAMA4BBgAAGAcAgza5OZl+W1q99UyfoATANDxCDBAGCgqviXUJQBAUBFgwlTuguJQlwAAQLsRYIBuhvsJAQgHBBhI4twV04x5dUyoSwCAkCLAILxccf8ZI+9nwj10AIAAg6tlZ2eHugQgrHDYDwgcAQZtxzd/AEAXQYBBixL2Vlw9kxADAOgCCDCAIdp6M8GWNBtIAcBQBJhuwMgTUTtBd7p5G+EDAPwRYMJYe29m19ZLeLvKycB8+ANA99Mj1AUAwZSdnd1lglNnyl1QrPRN04O6zYS9FfJMGx/wel8te0+bY4rCchyADpUdqzFJQyVJ23IuSZKKp+ZKki58t1aSNCfpSUnS5pgiSdKdd/1BcyPelCTFFPxFkvSnmF9J0lXb8n2pvfDd2qu249uj7ftCGFPwF/1pdWrQuxgI9sAgLFzv+SMdta3uhhsiAugsBBiEH66karOrDr+18m/HvUwAdCYCDMIWt+PvOjgRHUCgCDCAWj+huTtdzdQRTPxlc8IrYD4CDIzV2R+cXM3kj70mAEKJAINO0xEBwMRv/9LVH/6cPwIAgSHAIGi6yodwuF6+21lhjsMvHYMruIDAEGDCDLv9r9bRH/xcdg0AwUeAgVEC/fZ/+WErgkTnCtc9YQA6BwEmDHXXvTDB3AX/1bL3+AAGgC6MAAOEUHOHrwINTt01kAJAawgwHSTc7h0SyquB1sy5N6gnEHelkynD7XV0ua40DgC6HgJMF3D5h3+b/mhzK3yEAq87AF0IASbE2rLnghuooTvgJGoAwUSAgTG6+/1HLt/7xgnEANA6AgzQxbEHrnNx7g1gBgIM0BnC+PyRcD4RGUDHIcAAIdbSFVR88HeMlg5FBvtqtg4RxkEYuBIBBuhkvg/Qjrh/S5f/AO5muvt5WUBXRoCBH04eRVcTtNckey+AboUAAyDshfJGjADahwADAACMQ4DpwrrrSZzc0Kz7uNaei+76Gm4N5yEBnYMA01k4/g4AQNAQYACEJX7FGzAbAaYLCuROoFzGCZN11Os3kENXwTqBl0AEdC4CTBfF5cwAALSMAAOg2+PEcaD7IcAA6NaC+WOYBCGg6yDAoMOF46W0QGfiRnwIRwQYAABgHALMdeCGVQgHHDbpGloah0CvfmI80V0QYADgCoHcysBEnR1ignkeEuBDgGknjjkD3VuXvZXB/97Vm3tAIdwRYDoY3zwA8/E+BroeAoypsmP5BgajdfTrN1hXv3EVHdA1EWAMwsl3CDdd/TCOKQhh6I66dIDJzc3VzTffrJiYGE2ePFkHDx4MdUkA0CVxXh7CTZcNMG+88YYyMzO1cuVKHT58WOPGjZPb7VZNTU2oSwOAa2rPlUzNrdPa+TetXULdZfdeAUHSZQPM2rVr9Zvf/EYPP/ywkpOTtWnTJvXu3Vsvv/xyqEsDgA53+WGfDjl8fMVhsFD8mja/4I3r0SPUBTSnoaFB5eXlysrKsudFRkYqJSVFZWVlza5TX1+v+vp6+3Ftba0kyev1dkiN5xvqVH/xoiTpbH2dvF6v/fx1dU2SpKaIc9//t/6v8kZYkqTG842SpHONjfZ2fHVeuHhRZ+u/f1wfUd/8drxeqf7qbbW1n215Xl8fLn9eSc32wev12tuqb2E7vudpqjt31bbU1vGpt1p9Xun7cbjyeVvqg7xeNZ5v1LnGxma34+uD1+tVXV2TXb9vW4G8ri4f/8ufV7r6deR73pb64Bv/xvPf/xtcOZ6+bXm9Xr/XpW88A6r7yv5e9rpr6XlbHX/fGFzWB8n/Nel7Pfrq920nkLp9/bWf939fd829fg/98Fadv3NNi33wjb/v366l96BvLK6sv611X/4ezMrKUlZWlurqmjRw1/uSWn/9+v7tJOn/3u/W43nb7X8H3/P7XsNXbeuy+nz1/z6jVI89e3eb6vZ5/tf/x37e5jTVNf/aC+RvlyQN3/exPr9rbEC1dRe+13BLnyGtvQfb8rf8ym21+e9RB/Bt17Ks1htaXdBf/vIXS5K1f/9+v/lLliyxJk2a1Ow6K1eutCQxMTExMTExdYOpqqqq1azQJffAtEdWVpYyMzPtx01NTfr22281YMAARUREtHu7Xq9XiYmJqqqqktPpDEapXU449FEKj36GQx+l8OhnOPRRCo9+hkMfpeD107IsnT17VoMHD261XZcMMDfeeKOioqJUXV3tN7+6uloJCQnNruNwOORwOPzmxcXFBa0mp9PZrV94Unj0UQqPfoZDH6Xw6Gc49FEKj36GQx+l4PQzNjb2mm265Em80dHRmjhxooqKiux5TU1NKioqksvlCmFlAACgK+iSe2AkKTMzU2lpabr99ts1adIkPfvss6qrq9PDDz8c6tIAAECIddkAM2fOHH3zzTdasWKFPB6Pxo8frz179ig+Pr5T63A4HFq5cuVVh6e6k3DooxQe/QyHPkrh0c9w6KMUHv0Mhz5Knd/PCMu61nVKAAAAXUuXPAcGAACgNQQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4CRlJubq5tvvlkxMTGaPHmyDh482Gr77du3a+TIkYqJidGYMWO0e/fuTqq0/QLpY15eniIiIvymmJiYTqw2cPv27dMvfvELDR48WBEREdq5c+c11ykpKdGECRPkcDg0fPhw5eXldXid1yvQfpaUlFw1lhEREfJ4PJ1TcDvk5OToxz/+sfr166eBAwdq1qxZOnHixDXXM+l92Z4+mvi+3Lhxo8aOHWvfmdXlcuntt99udR2TxlEKvI8mjmNzVq9erYiICGVkZLTariPHM+wDzBtvvKHMzEytXLlShw8f1rhx4+R2u1VTU9Ns+/379+vBBx/U/PnzdeTIEc2aNUuzZs3SJ5980smVt12gfZS+vxX0119/bU9//vOfO7HiwNXV1WncuHHKzc1tU/uTJ08qNTVV06ZNU0VFhTIyMvToo4+qoKCggyu9PoH20+fEiRN+4zlw4MAOqvD6lZaWKj09XQcOHFBhYaEuXryoGTNmqK6ursV1THtftqePknnvyyFDhmj16tUqLy/XRx99pOnTp+u+++7T0aNHm21v2jhKgfdRMm8cr3To0CG9+OKLGju29V8G7/DxDM7vR5tr0qRJVnp6uv24sbHRGjx4sJWTk9Ns+7/7u7+zUlNT/eZNnjzZ+od/+IcOrfN6BNrHV155xYqNje2k6oJPkrVjx45W2yxdutQaPXq037w5c+ZYbre7AysLrrb0c+/evZYk67vvvuuUmjpCTU2NJckqLS1tsY2J78vLtaWPpr8vfW644QZr8+bNzS4zfRx9Wuuj6eN49uxZ64c//KFVWFho3X333dYTTzzRYtuOHs+w3gPT0NCg8vJypaSk2PMiIyOVkpKisrKyZtcpKyvzay9Jbre7xfah1p4+StK5c+c0bNgwJSYmXvPbhIlMG8frNX78eA0aNEg/+9nP9MEHH4S6nIDU1tZKkvr3799iG9PHsy19lMx+XzY2Nmrr1q2qq6tr8TftTB/HtvRRMnsc09PTlZqaetU4NaejxzOsA8x///d/q7Gx8aqfJ4iPj2/xHAGPxxNQ+1BrTx9HjBihl19+Wf/5n/+p1157TU1NTbrjjjv01VdfdUbJnaKlcfR6vTp//nyIqgq+QYMGadOmTXrzzTf15ptvKjExUVOnTtXhw4dDXVqbNDU1KSMjQz/5yU902223tdjOtPfl5draR1Pfl5WVlerbt68cDocWLFigHTt2KDk5udm2po5jIH00dRwlaevWrTp8+LBycnLa1L6jx7PL/hYSQsflcvl9e7jjjjs0atQovfjii3r66adDWBkCNWLECI0YMcJ+fMcdd+iLL77QunXr9Ic//CGElbVNenq6PvnkE73//vuhLqXDtLWPpr4vR4wYoYqKCtXW1urf//3flZaWptLS0hY/4E0USB9NHceqqio98cQTKiws7DInHYd1gLnxxhsVFRWl6upqv/nV1dVKSEhodp2EhISA2odae/p4pZ49e+pHP/qRPv/8844oMSRaGken06levXqFqKrOMWnSJCMCwaJFi7Rr1y7t27dPQ4YMabWtae9Ln0D6eCVT3pfR0dEaPny4JGnixIk6dOiQ1q9frxdffPGqtqaOYyB9vJIp41heXq6amhpNmDDBntfY2Kh9+/bphRdeUH19vaKiovzW6ejxDOtDSNHR0Zo4caKKiorseU1NTSoqKmrx+KXL5fJrL0mFhYWtHu8Mpfb08UqNjY2qrKzUoEGDOqrMTmfaOAZTRUVFlx5Ly7K0aNEi7dixQ8XFxUpKSrrmOqaNZ3v6eCVT35dNTU2qr69vdplp49iS1vp4JVPG8Z577lFlZaUqKirs6fbbb9fcuXNVUVFxVXiROmE8g3IqsMG2bt1qORwOKy8vzzp27Jj12GOPWXFxcZbH47Esy7LmzZtnLVu2zG7/wQcfWD169LB+97vfWcePH7dWrlxp9ezZ06qsrAxVF64p0D4+9dRTVkFBgfXFF19Y5eXl1gMPPGDFxMRYR48eDVUXruns2bPWkSNHrCNHjliSrLVr11pHjhyx/vznP1uWZVnLli2z5s2bZ7f/8ssvrd69e1tLliyxjh8/buXm5lpRUVHWnj17QtWFNgm0n+vWrbN27txpffbZZ1ZlZaX1xBNPWJGRkda7774bqi5c08KFC63Y2FirpKTE+vrrr+3pr3/9q93G9Pdle/po4vty2bJlVmlpqXXy5Enr448/tpYtW2ZFRERY77zzjmVZ5o+jZQXeRxPHsSVXXoXU2eMZ9gHGsizr+eeft4YOHWpFR0dbkyZNsg4cOGAvu/vuu620tDS/9tu2bbNuvfVWKzo62ho9erSVn5/fyRUHLpA+ZmRk2G3j4+Otn//859bhw4dDUHXb+S4XvnLy9SstLc26++67r1pn/PjxVnR0tPWDH/zAeuWVVzq97kAF2s/f/va31i233GLFxMRY/fv3t6ZOnWoVFxeHpvg2aq5/kvzGx/T3ZXv6aOL78pFHHrGGDRtmRUdHWzfddJN1zz332B/slmX+OFpW4H00cRxbcmWA6ezxjLAsywrOvhwAAIDOEdbnwAAAADMRYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOP8Pj9HSBN4inw8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT; SEE LATER FOR unit - unit ...\n",
    "print(\"Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default = too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on unit-unit distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "64d7bf07-e51a-4a08-8931-592a3acbf1c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.131938247693493\n"
     ]
    }
   ],
   "source": [
    "print(maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bc91a02a-52b2-4ac3-afc2-7f1650e32922",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 10 out of 4805 . Time = 0\n",
      "working on unit-unit angles for unit 269 out of 4805 . Time = 22\n",
      "working on unit-unit angles for unit 504 out of 4805 . Time = 44\n",
      "working on unit-unit angles for unit 774 out of 4805 . Time = 66\n",
      "working on unit-unit angles for unit 974 out of 4805 . Time = 87\n",
      "working on unit-unit angles for unit 1174 out of 4805 . Time = 106\n",
      "working on unit-unit angles for unit 1375 out of 4805 . Time = 124\n",
      "working on unit-unit angles for unit 1576 out of 4805 . Time = 141\n",
      "working on unit-unit angles for unit 1831 out of 4805 . Time = 156\n",
      "working on unit-unit angles for unit 2031 out of 4805 . Time = 171\n",
      "working on unit-unit angles for unit 2231 out of 4805 . Time = 185\n",
      "working on unit-unit angles for unit 2446 out of 4805 . Time = 197\n",
      "working on unit-unit angles for unit 2646 out of 4805 . Time = 208\n",
      "working on unit-unit angles for unit 2885 out of 4805 . Time = 218\n",
      "working on unit-unit angles for unit 3101 out of 4805 . Time = 227\n",
      "working on unit-unit angles for unit 3353 out of 4805 . Time = 237\n",
      "working on unit-unit angles for unit 3553 out of 4805 . Time = 245\n",
      "working on unit-unit angles for unit 3764 out of 4805 . Time = 251\n",
      "working on unit-unit angles for unit 4017 out of 4805 . Time = 255\n",
      "working on unit-unit angles for unit 4290 out of 4805 . Time = 258\n",
      "working on unit-unit angles for unit 4551 out of 4805 . Time = 260\n",
      "working on unit-unit angles for unit 4781 out of 4805 . Time = 261\n",
      "All done with angles; total elapsed seconds = 261\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %200 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "plt.hist(uuAngle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "83cf363c-a1db-4c15-924a-df668c3507a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "774870.5 12 9298446.0 12.0 10077331.0\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, statePop, statePop/aDP, trueStatePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4e4690c9-a317-4944-b5fa-8ba6af7c26e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9298446.0 9298446 0.9227091975047759\n"
     ]
    }
   ],
   "source": [
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), np.sum(HDweight)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3b81c075-4d07-45ab-a70b-2ba7a128670f",
   "metadata": {},
   "outputs": [],
   "source": [
    "toSquishCountiesList = list()   #if we did not have any clipPoly's to move in\n",
    "nClips = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e4440c23-8566-43d3-b009-0590287ea844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)\n",
    "#for c in range(nCounties):\n",
    "#    for cc in opt2nbrCtyList[c]:\n",
    "#        plotPoly(countyGeom[cc])\n",
    "#    plotCenter(c,countyGeom[c])\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3214fdc5-d104-4164-9478-7ab097ce01e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 1761.069 of targetWedgePop\n",
      "I am working on HD number 10 of 4805 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 504 of 4805 HDs.  Elapsed sec = 53\n",
      "I am working on HD number 974 of 4805 HDs.  Elapsed sec = 95\n",
      "I am working on HD number 1375 of 4805 HDs.  Elapsed sec = 136\n",
      "I am working on HD number 1831 of 4805 HDs.  Elapsed sec = 185\n",
      "I am working on HD number 2231 of 4805 HDs.  Elapsed sec = 252\n",
      "I am working on HD number 2646 of 4805 HDs.  Elapsed sec = 294\n",
      "I am working on HD number 3101 of 4805 HDs.  Elapsed sec = 327\n",
      "I am working on HD number 3553 of 4805 HDs.  Elapsed sec = 362\n",
      "I am working on HD number 4017 of 4805 HDs.  Elapsed sec = 400\n",
      "I am working on HD number 4551 of 4805 HDs.  Elapsed sec = 434\n",
      "453.215 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\n",
      "unit avg and sd usage are 0.99823 0.11432\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"unit avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "aec0cf55-112b-4eba-93c6-9cf1d1b87aae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_21Mar.csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b7a38aff-e4c4-498a-9f63-b6d5d02ab158",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9227091975047759\n"
     ]
    }
   ],
   "source": [
    "print(np.average(HDweight) * nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "253554da-8842-470e-b9b3-5626823059b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\n",
      "working on cluster no 0 containing counties [43]\n",
      "halfway there! last use was 0.62785\n",
      "our final fractional capture area to normalize this cluster's usage is 0.26146\n",
      "working on cluster no 1 containing counties [44]\n",
      "halfway there! last use was 0.79483\n",
      "our final fractional capture area to normalize this cluster's usage is 0.22671\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#skip this block, use one below if I already CREATED HDVTDLIST'S FROM POLYGONS and there are non-vtd units but all tracts are vtds\n",
    "print(\"Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\")\n",
    "#origHDlist = [list() for t in range(nHDs)],\n",
    "splitTractNo, splitTractUse = [-999]*nHDs, [0.]*nHDs\n",
    "origHDpop = [0. for t in range(nHDs)]  #these will be based on captured Census vtd's\n",
    "HDpoly, HDdiam = [dummyPoly for t in range(nHDs)], [0. for t in range(nHDs)]\n",
    "\n",
    "popHDlist = list()  #rebuild here again with the geometries\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001 :  #HD poly was actually drawn  #[(HDradius[t][w]+0.001*maxD) for w in range(4)]\n",
    "        HDpoly[t] = buildPoly(hdCP[t], HDradius[t], HDangle[t])  #expand to catch the units on HD edge\n",
    "        HDdiam[t] = HDpoly[t].area **0.5  #approximate, for later scoring purposes of underuse * distance\n",
    "        popHDlist.append(t)\n",
    "\n",
    "\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:\n",
    "        i -=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t],0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]) )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t],1.3)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "868ec654-07ad-4925-bea5-105e8fe9bc59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDtractList = [list(set(oldHDtractList[t] ) ) for t in range(nHDs)] #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6f48a4-26fe-46a0-84e0-61cf3e042780",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3/10/24 - Georgia -- not sure why unitUse is off here when I pull from original HDtractList.  Used polygons instead, but still off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63418340-35f5-4a32-a0aa-6b04478b3f57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now update vtd lists to either fully take or shed CCBs\n",
      "working on cluster no 1 containing counties [22, 145, 40, 26]\n",
      "halfway there! last use was 0.9714\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.17306 from 202 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 0 in or out\n",
      "working on cluster no 2 containing counties [118, 67, 138, 126]\n",
      "halfway there! last use was 0.98332\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.3148 from 203 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 1 in or out\n",
      "working on cluster no 3 containing counties [24]\n",
      "halfway there! last use was 0.95399\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.30829 from 238 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 2 in or out\n"
     ]
    }
   ],
   "source": [
    "#new for FL - alt to above, using pop-capture threshold on vtd Lists, not area threshold on HDpoly's for including CCBs\n",
    "oldHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "print(\"we will now update vtd lists to either fully take or shed CCBs\")\n",
    "CCBuse, CCBpopFracThreshold = [0. for CCB in CCBlist], [0.0001 for CCB in CCBlist]\n",
    "for j,CCBl in enumerate(CCBlist):\n",
    "    CCBtL = list()  #list of tracts in the CCB\n",
    "    for c in CCBl:\n",
    "        CCBtL += countyTractList[c]\n",
    "    addList, nIntersections = list(), 0\n",
    "    print(\"working on cluster no\",j+1,\"containing counties\",CCBl )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] in CCBl:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            cPop = 0\n",
    "            for v in CCBtL:\n",
    "                if v in oldHDtractList[t]:\n",
    "                    cPop += tractPop[v]\n",
    "            HDfrac[t] = cPop / CCBpop[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #0.5 ... is to land near avg AFTER last added\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        addList.append(t)\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBpopFracThreshold[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture pop to normalize this cluster's usage is\",r5(CCBpopFracThreshold[j]),\n",
    "              \"from\",nIntersections,\"intersecting HDs\" )\n",
    "    print(\"Now I will update vtd lists to move full CCB\",j,\"in or out\")\n",
    "    for t in popHDlist:\n",
    "        if HDfrac[t] > 0:\n",
    "            HDtractList[t] = list(set(HDtractList[t]).difference(CCBtL))\n",
    "            if t in addList:\n",
    "                HDtractList[t] += CCBtL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e2b458c5-1240-49bc-9c8c-752d3604eaff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2157\n"
     ]
    }
   ],
   "source": [
    "print(len(origHDlist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "beb8908f-1855-4224-9cbe-6f484a193f5a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping - is this from option 1??\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c1f835-ecad-444a-9f0e-0ab5c0e394e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4328e35e-089d-4f81-9987-a2cf0785a4ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\n",
      "Lets just pull in the HDtractLists from that HD2 code and use them\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 2698 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "orig unit avg and sd usage are 0.94095 0.25629\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now re-calculate HD pops based on unit assignments\n"
     ]
    },
    {
     "data": {
      "image/png": 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bN09xcXGKjY3V/Pnz1a9fP40cObL5byEAAGhzAgqU1atXS5KGDx/ud/zZZ5/VPffcI0lasGCBampqNHPmTJWXl2vw4MHKy8tTVFSUb/3y5csVGhqqiRMnqqamRiNGjFB2drZCQkIu7NYAAIB24YLeByVYeB8UoPXxPigALlSrvQ8KAABASyBQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmhAZ7AADB12PhG8EeAQD8cAYFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGBOaLAHANCyeix8I9gjAEDAAj6DsmXLFt12221KSEiQx+PRa6+95ne9c04PP/ywEhISFBkZqeHDh2vPnj1+a2prazV79mx17dpVnTp10vjx43X48OELuiEAAKD9CDhQjh07pgEDBmjlypVnvX7p0qVatmyZVq5cqe3bt8vr9WrUqFGqqqryrcnIyFBubq5ycnK0detWVVdXa9y4caqvr2/6LQEAAO1GwA/xjBkzRmPGjDnrdc45rVixQosXL9Ydd9whSXruuecUHx+vF154QdOnT1dFRYXWrl2rDRs2aOTIkZKk559/XklJSXr77bc1evToC7g5AACgPWjWJ8kePHhQpaWlSk9P9x2LiIjQsGHDtG3bNklSYWGhTpw44bcmISFBKSkpvjVnqq2tVWVlpd8FAAC0X80aKKWlpZKk+Ph4v+Px8fG+60pLSxUeHq4uXbr84JozZWVlKSYmxndJSkpqzrEBAIAxLfIyY4/H4/e1c67BsTOda82iRYtUUVHhuxQXFzfbrAAAwJ5mDRSv1ytJDc6ElJWV+c6qeL1e1dXVqby8/AfXnCkiIkLR0dF+FwAA0H41a6D07NlTXq9X+fn5vmN1dXUqKChQWlqaJCk1NVVhYWF+a0pKSrR7927fGgAAcHEL+FU81dXV+uKLL3xfHzx4ULt27VJsbKyuvPJKZWRkKDMzU8nJyUpOTlZmZqY6duyoyZMnS5JiYmI0bdo0zZs3T3FxcYqNjdX8+fPVr18/36t6AADAxS3gQNmxY4duvfVW39dz586VJE2dOlXZ2dlasGCBampqNHPmTJWXl2vw4MHKy8tTVFSU73uWL1+u0NBQTZw4UTU1NRoxYoSys7MVEhLSDDcJAAC0dR7nnAv2EIGqrKxUTEyMKioqeD4KcB4X81vdH3p0bLBHAPA/Arn/5sMCAQCAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJgTGuwBgLakMZ8MzCfoAsCF4wwKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA4vMwaaGS9FBoALxxkUAABgDmdQgDasMWdrAKAt4gwKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHN4FQ9gFK/QAXAx4wwKAAAwhzMoQBBwdgQAzo0zKAAAwBwCBQAAmMNDPADaLT64EWi7OIMCAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDm/UhosCn30DAG0LgYI2j/gAgPaHh3gAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmMOreGAar9ABgIsTgYKgIT4AAD+Eh3gAAIA5nEFBi+DsCADgQnAGBQAAmEOgAAAAc3iIBwHj4RtcbBrzN3/o0bGtMAlw8SBQAKAZEDFA8yJQ4IezIwAACwgUABc1ohywiSfJAgAAcwgUAABgDoECAADM4TkoQdZcz/znFQSAffx3CjQegXIR4cmAAM5ENMGqoAbKqlWr9Nhjj6mkpETXXnutVqxYoZtvvjmYIwFAUFn8H4nWnIkzxjgtaIHyt7/9TRkZGVq1apWGDh2qv/zlLxozZoyKiop05ZVXBmsskyz+gwUALeFi/veO8PLncc65YPziwYMH67rrrtPq1at9x/r27asJEyYoKyvrnN9bWVmpmJgYVVRUKDo6uqVHbVEX83+MAGBdawbBxRAogdx/B+UMSl1dnQoLC7Vw4UK/4+np6dq2bVuD9bW1taqtrfV9XVFRIen7G9oSUh76f83yc3Y/Mvq8a07VHm+W3wUAaH5X/ualYI/gpzHzNOa+pzH3c435OYE6fb/dmHMjQQmUo0ePqr6+XvHx8X7H4+PjVVpa2mB9VlaWHnnkkQbHk5KSWmzG5hCzItgTAAAuNs1139OS92FVVVWKiYk555qgPknW4/H4fe2ca3BMkhYtWqS5c+f6vj516pS+/fZbxcXFnXV9MFVWViopKUnFxcVt/uEnK9jT5sV+Nj/2tHmxn83L0n4651RVVaWEhITzrg1KoHTt2lUhISENzpaUlZU1OKsiSREREYqIiPA7dumll7bkiBcsOjo66H8I7Q172rzYz+bHnjYv9rN5WdnP8505OS0o7yQbHh6u1NRU5efn+x3Pz89XWlpaMEYCAACGBO0hnrlz52rKlCkaNGiQhgwZojVr1uirr77SjBkzgjUSAAAwImiBMmnSJH3zzTf64x//qJKSEqWkpGjTpk3q3r17sEZqFhEREXrooYcaPCSFpmNPmxf72fzY0+bFfjavtrqfQXsfFAAAgB/CpxkDAABzCBQAAGAOgQIAAMwhUAAAgDkEShOsWrVKPXv2VIcOHZSamqr33nvvB9du3rxZHo+nweWzzz5rxYntC2RPpe8/n2nx4sXq3r27IiIi1Lt3b61bt66VprUvkP285557zvo3eu2117bixLYF+ve5ceNGDRgwQB07dlS3bt1077336ptvvmmladuGQPf06aefVt++fRUZGak+ffpo/fr1rTSpfVu2bNFtt92mhIQEeTwevfbaa+f9noKCAqWmpqpDhw7q1auX/vznP7f8oIFyCEhOTo4LCwtzzzzzjCsqKnJz5sxxnTp1cl9++eVZ17/77rtOktu7d68rKSnxXU6ePNnKk9sV6J4659z48ePd4MGDXX5+vjt48KD78MMP3fvvv9+KU9sV6H5+9913fn+bxcXFLjY21j300EOtO7hRge7ne++95y655BL35JNPugMHDrj33nvPXXvttW7ChAmtPLldge7pqlWrXFRUlMvJyXH79+93L774ouvcubN7/fXXW3lymzZt2uQWL17sXnnlFSfJ5ebmnnP9gQMHXMeOHd2cOXNcUVGRe+aZZ1xYWJh7+eWXW2fgRiJQAnTDDTe4GTNm+B27+uqr3cKFC8+6/nSglJeXt8J0bVOge/rmm2+6mJgY980337TGeG1OoPt5ptzcXOfxeNyhQ4daYrw2J9D9fOyxx1yvXr38jj311FMuMTGxxWZsawLd0yFDhrj58+f7HZszZ44bOnRoi83YVjUmUBYsWOCuvvpqv2PTp093N954YwtOFjge4glAXV2dCgsLlZ6e7nc8PT1d27ZtO+f3Dhw4UN26ddOIESP07rvvtuSYbUpT9vT111/XoEGDtHTpUl1xxRW66qqrNH/+fNXU1LTGyKZdyN/oaWvXrtXIkSPb/JsmNoem7GdaWpoOHz6sTZs2yTmnr7/+Wi+//LLGjh3bGiOb15Q9ra2tVYcOHfyORUZG6t///rdOnDjRYrO2V//6178a7P/o0aO1Y8cOU/tJoATg6NGjqq+vb/CBhvHx8Q0++PC0bt26ac2aNXrllVf06quvqk+fPhoxYoS2bNnSGiOb15Q9PXDggLZu3ardu3crNzdXK1as0Msvv6xZs2a1xsimNWU//1dJSYnefPNN3XfffS01YpvSlP1MS0vTxo0bNWnSJIWHh8vr9erSSy/Vn/70p9YY2bym7Ono0aP117/+VYWFhXLOaceOHVq3bp1OnDiho0ePtsbY7UppaelZ9//kyZOm9jNob3Xflnk8Hr+vnXMNjp3Wp08f9enTx/f1kCFDVFxcrMcff1y33HJLi87ZlgSyp6dOnZLH49HGjRt9n4q5bNky3XnnnXr66acVGRnZ4vNaF8h+/q/s7GxdeumlmjBhQgtN1jYFsp9FRUX69a9/rT/84Q8aPXq0SkpK9MADD2jGjBlau3Zta4zbJgSyp7///e9VWlqqG2+8Uc45xcfH65577tHSpUsVEhLSGuO2O2fb/7MdDybOoASga9euCgkJaVD5ZWVlDWr0XG688UZ9/vnnzT1em9SUPe3WrZuuuOIKv4/s7tu3r5xzOnz4cIvOa92F/I0657Ru3TpNmTJF4eHhLTlmm9GU/czKytLQoUP1wAMPqH///ho9erRWrVqldevWqaSkpDXGNq0pexoZGal169bp+PHjOnTokL766iv16NFDUVFR6tq1a2uM3a54vd6z7n9oaKji4uKCNFVDBEoAwsPDlZqaqvz8fL/j+fn5SktLa/TP2blzp7p169bc47VJTdnToUOH6siRI6qurvYd27dvny655BIlJia26LzWXcjfaEFBgb744gtNmzatJUdsU5qyn8ePH9cll/j/03r6//IdH312QX+jYWFhSkxMVEhIiHJycjRu3LgGe43zGzJkSIP9z8vL06BBgxQWFhakqc4iOM/NbbtOvzxu7dq1rqioyGVkZLhOnTr5XvGwcOFCN2XKFN/65cuXu9zcXLdv3z63e/dut3DhQifJvfLKK8G6CeYEuqdVVVUuMTHR3XnnnW7Pnj2uoKDAJScnu/vuuy9YN8GUQPfztLvvvtsNHjy4tcc1L9D9fPbZZ11oaKhbtWqV279/v9u6dasbNGiQu+GGG4J1E8wJdE/37t3rNmzY4Pbt2+c+/PBDN2nSJBcbG+sOHjwYpFtgS1VVldu5c6fbuXOnk+SWLVvmdu7c6XvZ9pn7efplxr/5zW9cUVGRW7t2LS8zbi+efvpp1717dxceHu6uu+46V1BQ4Ltu6tSpbtiwYb6vlyxZ4nr37u06dOjgunTp4m666Sb3xhtvBGFq2wLZU+ec+/TTT93IkSNdZGSkS0xMdHPnznXHjx9v5antCnQ/v/vuOxcZGenWrFnTypO2DYHu51NPPeWuueYaFxkZ6bp16+buuusud/jw4Vae2rZA9rSoqMj9+Mc/dpGRkS46Otrdfvvt7rPPPgvC1DadfjuLMy9Tp051zp39b3Tz5s1u4MCBLjw83PXo0cOtXr269Qc/D49znHMEAAC28OAdAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAz5YtW3TbbbcpISFBHo9Hr732WsA/wzmnxx9/XFdddZUiIiKUlJSkzMzMgH4Gn2YMAAB8jh07pgEDBujee+/Vz3/+8yb9jDlz5igvL0+PP/64+vXrp4qKCh09ejSgn8E7yQIAgLPyeDzKzc3VhAkTfMfq6ur0u9/9Ths3btR3332nlJQULVmyRMOHD5ckffrpp+rfv792796tPn36NPl38xAPAABotHvvvVfvv/++cnJy9PHHH+sXv/iFfvrTn+rzzz+XJP3jH/9Qr1699M9//lM9e/ZUjx49dN999+nbb78N6PcQKAAAoFH279+vF198US+99JJuvvlm9e7dW/Pnz9dNN92kZ599VpJ04MABffnll3rppZe0fv16ZWdnq7CwUHfeeWdAv4vnoAAAgEb56KOP5JzTVVdd5Xe8trZWcXFxkqRTp06ptrZW69ev961bu3atUlNTtXfv3kY/7EOgAACARjl16pRCQkJUWFiokJAQv+s6d+4sSerWrZtCQ0P9IqZv376SpK+++opAAQAAzWvgwIGqr69XWVmZbr755rOuGTp0qE6ePKn9+/erd+/ekqR9+/ZJkrp3797o38WreAAAgE91dbW++OILSd8HybJly3TrrbcqNjZWV155pe6++269//77euKJJzRw4EAdPXpU77zzjvr166ef/exnOnXqlK6//np17txZK1as0KlTpzRr1ixFR0crLy+v0XMQKAAAwGfz5s269dZbGxyfOnWqsrOzdeLECf3f//2f1q9fr//85z+Ki4vTkCFD9Mgjj6hfv36SpCNHjmj27NnKy8tTp06dNGbMGD3xxBOKjY1t9BwECgAAMIeXGQMAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOf8fdlzekSOwhggAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 176742.0 originally had use of 0.6927301840156793\n",
      "CCB cluster 1 with pop 102191.0 originally had use of 0.9326155527100091\n",
      "CCB cluster 2 with pop 295291.0 originally had use of 0.7581629715265193\n"
     ]
    }
   ],
   "source": [
    "#ALTERNATE RESTART FROM A DECENT HDtractList - this may not be robust for states with county clusters\n",
    "print(\"Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\")\n",
    "print(\"Lets just pull in the HDtractLists from that HD2 code and use them\")\n",
    "\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDtractList\"] #HDvtdList\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "# HDvPop = inDF[\"HDvPop\"].to_list()  #don't care; will rebuild from units\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD\n",
    "\n",
    "print(\"Now re-calculate HD pops based on unit assignments\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()        \n",
    "\n",
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])\n",
    "\n",
    "origHDlist   = [HDunitList[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "origHDpop =     HDvPop.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "22916ca4-1217-4231-9f11-88110a1a7cda",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 154316.0 originally had use of 1.0114786922459924\n",
      "CCB cluster 1 with pop 24060.0 originally had use of 0.8496180974756382\n",
      "CCB cluster 2 with pop 88252.0 originally had use of 0.955221549923498\n",
      "CCB cluster 3 with pop 125341.0 originally had use of 0.8549454392701692\n",
      "CCB cluster 4 with pop 160383.0 originally had use of 0.9413095478534921\n"
     ]
    }
   ],
   "source": [
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "5b99795f-d030-456d-9201-6519b739b472",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999998\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\n",
      "this optional RESTART block pulls in an existing vtdlist for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv ./2024state_HD_output/FL7211needsEnclaveRemoval.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 7211 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "working on converting HD 3000 from vtd list to unit list\n",
      "working on converting HD 3500 from vtd list to unit list\n",
      "working on converting HD 4000 from vtd list to unit list\n",
      "working on converting HD 4500 from vtd list to unit list\n",
      "working on converting HD 5000 from vtd list to unit list\n",
      "working on converting HD 5500 from vtd list to unit list\n",
      "working on converting HD 6000 from vtd list to unit list\n",
      "working on converting HD 6500 from vtd list to unit list\n",
      "working on converting HD 7000 from vtd list to unit list\n",
      "orig unit avg and sd usage are 1.00435 0.16718\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\")\n",
    "print(\"this optional RESTART block pulls in an existing vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "\n",
    "#infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv\")\n",
    "#inDF = pd.read_csv(infile)\n",
    "#vtdListString = inDF[\"HDvtdList\"]\n",
    "#nHDs = len(vtdListString)\n",
    "#inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "#HDweight = inDF[\"HDweight\"]\n",
    "#HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "#hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "#hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "#print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 4207 populated HDs\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 10 time is now 0\n",
      "working on HD 876 time is now 20\n",
      "working on HD 1581 time is now 29\n",
      "working on HD 2338 time is now 48\n",
      "working on HD 3149 time is now 82\n",
      "working on HD 3877 time is now 126\n",
      "working on HD 4798 time is now 158\n",
      "out of 4207 total HDs, there were 3552.0 contiguous and 3678.0 complement-contiguous HDs\n",
      "97 HDs had both discontiguity problems, while enclave-only = 432 and discontig only= 558\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 655 432\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE - continue here for option 1 or option 2 drawing method\n",
    "# 3/4/24 - for FL, using 6 or unspecified loops now yields same results\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,6) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 736126 district pop vs 774870 target\n",
      "we'll also trim any HD with total islands' pop <= 15497\n",
      "working on HD 27\n",
      "working on HD 1757\n",
      "working on HD 3081\n",
      "working on HD 4465\n",
      "Out of 655 discontig HDs 655 had discontig HDs.\n",
      "Of these, 594 won't be under 736126 after all minor discontigys were shed, while 61 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 27\n",
      "reclassifying HD 3681\n",
      "There are 61 HDs still with both discontinuity problems\n",
      "Additionally, 91 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 25\n",
      "working on enclave-generating HD 3863\n",
      "Out of 523 HDpolys that generated enclaves, 0 had contiguous adjrs, while 403 neighbored a state boundary while 120 had only internal enclaves\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required 0\n"
     ]
    }
   ],
   "source": [
    "print(\"1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")\n",
    " \n",
    "plotEnclaves = input(\"enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required\")\n",
    "if str(plotEnclaves) == str(1):\n",
    "    for i,t in enumerate(internals): \n",
    "        if i%500 == 0:\n",
    "            print(\"computing full enclave lists for HD\",t)\n",
    "        fullEnclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        tEpop = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in fullEnclaveLists ] ) \n",
    "        plt.scatter(HDvPop[t],tEpop)\n",
    "    plt.plot([aDP,aDP],[0,5000],ls=\"--\")\n",
    "    print(\"Here are the total enclave pops for enclaving HDs not touching the state boundary vs. the original HD pop\")\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 813614 district pop vs 774870 target\n",
      "working on enclave-y HD 65 . We have evaluated 0 of 523 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 3224 . We have evaluated 100 of 523 enclavy HDs.Time is now 15\n",
      "working on enclave-y HD 1816 . We have evaluated 200 of 523 enclavy HDs.Time is now 25\n",
      "working on enclave-y HD 3058 . We have evaluated 300 of 523 enclavy HDs.Time is now 36\n",
      "working on enclave-y HD 3691 . We have evaluated 400 of 523 enclavy HDs.Time is now 43\n",
      "working on enclave-y HD 3167 . We have evaluated 500 of 523 enclavy HDs.Time is now 52\n",
      "Out of 523 HDs with enclaves 523 had enclaves.\n",
      "Of these, 472 wouldn't be over 813614 if all enclaves filled, while 51 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120 403 97 67\n",
      "51 51 472\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00759 0.12229\n",
      "CCB cluster 0 = unit 4143 with pop 154316.0 now has use of 1.0114786922459924\n",
      "CCB cluster 1 = unit 4144 with pop 24060.0 now has use of 0.8496180974756382\n",
      "CCB cluster 2 = unit 4145 with pop 88252.0 now has use of 0.955221549923498\n",
      "CCB cluster 3 = unit 4146 with pop 125341.0 now has use of 0.8549454392701692\n",
      "CCB cluster 4 = unit 4147 with pop 160383.0 now has use of 0.9413095478534921\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7729308-3cd8-4c12-9199-f400f59e6f2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 51 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 10\n",
      "working on avg unit-to-HDcp dist for HD 976\n",
      "working on avg unit-to-HDcp dist for HD 1837\n",
      "working on avg unit-to-HDcp dist for HD 2654\n",
      "working on avg unit-to-HDcp dist for HD 3565\n",
      "working on avg unit-to-HDcp dist for HD 4567\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([4144, 4145, 4146, 4147] )  #lock in the clusters  #update this for each state\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 51 HDs\n",
      "working on HD 480 cantFill 0 of 51 .Time is now 0\n",
      "working on HD 3178 cantFill 20 of 51 .Time is now 0\n",
      "working on HD 3288 cantFill 40 of 51 .Time is now 0\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.00659 0.12209\n",
      "CCB cluster 0 = unit 4143 with pop 154316.0 now has use of 1.0114786922459924\n",
      "CCB cluster 1 = unit 4144 with pop 24060.0 now has use of 0.8496180974756382\n",
      "CCB cluster 2 = unit 4145 with pop 88252.0 now has use of 0.955221549923498\n",
      "CCB cluster 3 = unit 4146 with pop 125341.0 now has use of 0.8549454392701692\n",
      "CCB cluster 4 = unit 4147 with pop 160383.0 now has use of 0.9413095478534921\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "out of 4207 total HDs, there were 4124 79 0 4 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 10\n",
      "working on current HD diam for HD number 976\n",
      "working on current HD diam for HD number 1837\n",
      "working on current HD diam for HD number 2654\n",
      "working on current HD diam for HD number 3565\n",
      "working on current HD diam for HD number 4567\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "print(\"here is the histogram of HD diameter = sqrt(area)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than  38744 but less than 193717\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 83 HDs to triage in this block\n",
      "working on swell-filling discontig HD 102 our 1 th HD we think we can swell out of 83\n",
      "working on swell-filling discontig HD 2243 our 41 th HD we think we can swell out of 83\n",
      "working on swell-filling discontig HD 257 our 81 th HD we think we can swell out of 83\n",
      "we partially regrew 51 HDs, leaving 32 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than \",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList) )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.75 * aDP or contigPop > 2.*aDP - minPostFixPop:  \n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 1.00659 0.12209\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00749 0.12027\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jk1q4cKEsFouio6NVWFhoW8hcXl4ud/dfTxwdO3ZMI0aMsD1etmyZli1bpoSEBO3YsUOS9Oabb0r65eKCf2/dunV64oknZDKZtG3bNlu4Cg8PV3JyshYsWOBo+wAAoBNq0aLljIwMZWRkNPvcpRBzSUREhKxW61XrXev58PBw7dy506EeAQAALuFeWgAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPAIPAAAwPC6tHUDAOAsh0+cdUndnt1NCvP3dkltAK2DwAOgw+vZ3STvrh6aU1DmkvreXT207bkEQg/QgRF4AHR4Yf7e2vZcgk7X1ju99uETZzWnoEyna+sJPEAH1qI1PKtXr1ZERIS8vLwUFxenvXv3XnHsV199peTkZEVERMjNzU25ubktqnnhwgXNnDlTvXr1Uo8ePZScnKzKysqWtA/AgML8vRUZ5uf0bUBQj7aeGgAncDjwFBQUyGw2KysrS6WlpYqKilJSUpJOnDjR7Phz586pX79+WrJkiUJCQlpcc+7cufrwww+1YcMG7dy5U8eOHdPDDz/saPsAAKATcjjw5OTkKD09XWlpaRo6dKjy8vLUrVs3rV27ttnxo0eP1tKlSzV58mR5enq2qGZ1dbXWrFmjnJwc3XPPPYqJidG6deu0a9cu7d6929EpAACATsahwFNfX6+SkhIlJib+WsDdXYmJiSouLm5RA9dTs6SkRA0NDXZjBg8erJtvvvmKr1tXV6eamhq7DQAAdE4OBZ5Tp06psbFRwcHBdvuDg4NlsVha1MD11LRYLDKZTPL397/u183Ozpafn59tCw8Pb1F/AACg4zPshQczMzNVXV1t244ePdrWLQEAgDbi0NfSAwMD5eHhcdm3oyorK6+4INkZNUNCQlRfX6+qqiq7szxXe11PT88rrhkCAACdi0NneEwmk2JiYlRUVGTb19TUpKKiIsXHx7eogeupGRMTo65du9qNOXTokMrLy1v8ugAAoPNw+MKDZrNZqampGjVqlGJjY5Wbm6va2lqlpaVJkqZNm6awsDBlZ2dL+mVR8oEDB2w/V1RUqKysTD169NCAAQOuq6afn5+mT58us9msgIAA+fr6atasWYqPj9ftt9/ulF8EAAAwLocDT0pKik6ePKmFCxfKYrEoOjpahYWFtkXH5eXlcnf/9cTRsWPHNGLECNvjZcuWadmyZUpISNCOHTuuq6Ykvfbaa3J3d1dycrLq6uqUlJSkN954o6XzBgAAnYib1Wq1tnUTraGmpkZ+fn6qrq6Wr69vW7cD3LD9FdV6YOVn2jzrTkWG+bV1O4bF7xloW856/zbst7QAAAAuIfAAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADDI/AAAADD69LWDQBGV1F1Xqdr651e9/CJs06vCQBGReABXKii6rwSl+/U+YZGl9T37uqhnt1NLqkNAEZC4AFc6HRtvc43NCo3JVoDgno4vX7P7iaF+Xs7vS4AGA2BB2gFA4J6KDLMr63bAIBOi0XLAADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8FoUeFavXq2IiAh5eXkpLi5Oe/fuver4DRs2aPDgwfLy8tKwYcO0ZcsWu+fd3Nya3ZYuXWobExERcdnzS5YsaUn7AACgk3E48BQUFMhsNisrK0ulpaWKiopSUlKSTpw40ez4Xbt2acqUKZo+fbq++OILTZo0SZMmTdL+/fttY44fP263rV27Vm5ubkpOTrar9dJLL9mNmzVrlqPtAwCATsjhwJOTk6P09HSlpaVp6NChysvLU7du3bR27dpmx69YsULjx4/X888/ryFDhujll1/WyJEjtWrVKtuYkJAQu+3Pf/6z7r77bvXr18+ulo+Pj9247t27O9o+AADohBwKPPX19SopKVFiYuKvBdzdlZiYqOLi4maPKS4uthsvSUlJSVccX1lZqY8++kjTp0+/7LklS5aoV69eGjFihJYuXaqLFy860j4AAOikujgy+NSpU2psbFRwcLDd/uDgYB08eLDZYywWS7PjLRZLs+Pfffdd+fj46OGHH7bb/+yzz2rkyJEKCAjQrl27lJmZqePHjysnJ6fZOnV1daqrq7M9rqmpueb8AACAMTkUeFrD2rVrNXXqVHl5edntN5vNtp+HDx8uk8mkZ555RtnZ2fL09LysTnZ2thYvXuzyfgEAQPvn0EdagYGB8vDwUGVlpd3+yspKhYSENHtMSEjIdY//9NNPdejQIT311FPX7CUuLk4XL17U999/3+zzmZmZqq6utm1Hjx69Zk0AAGBMDgUek8mkmJgYFRUV2fY1NTWpqKhI8fHxzR4THx9vN16Stm7d2uz4NWvWKCYmRlFRUdfspaysTO7u7goKCmr2eU9PT/n6+tptAACgc3L4Iy2z2azU1FSNGjVKsbGxys3NVW1trdLS0iRJ06ZNU1hYmLKzsyVJs2fPVkJCgpYvX66JEydq/fr12rdvn9566y27ujU1NdqwYYOWL19+2WsWFxdrz549uvvuu+Xj46Pi4mLNnTtXjz/+uHr27NmSeQMAgE7E4cCTkpKikydPauHChbJYLIqOjlZhYaFtYXJ5ebnc3X89cTRmzBjl5+drwYIFmj9/vgYOHKhNmzYpMjLSru769etltVo1ZcqUy17T09NT69ev16JFi1RXV6e+fftq7ty5dut6AAAArsTNarVa27qJ1lBTUyM/Pz9VV1fz8RZazf6Kaj2w8jNtnnWnIsP82rodtAD/DoG25az3b+6lBQAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADI/AAwAADK9LWzcAAB3B4RNnnV6zZ3eTwvy9nV4XwOUIPABwFT27m+Td1UNzCsqcXtu7q4e2PZdA6AFaAYEHAK4izN9b255L0OnaeqfWPXzirOYUlOl0bT2BB2gFBB4AuIYwf29CCdDBsWgZAAAYHoEHAAAYHoEHAAAYHoEHAAAYHoEHAAAYHoEHAAAYHoEHAAAYHtfhQYdSUXXe6ReAk7jEPwAYHYEHHUZF1XklLt+p8w2NTq/NJf4BwNgIPOgwTtfW63xDo3JTojUgqIfT6nKJfwAwPgIPOpwBQT0UGebX1m0AADoQFi0DAADDI/AAAADDI/AAAADDI/AAAADDa1HgWb16tSIiIuTl5aW4uDjt3bv3quM3bNigwYMHy8vLS8OGDdOWLVvsnn/iiSfk5uZmt40fP95uzM8//6ypU6fK19dX/v7+mj59us6ePduS9gEAQCfjcOApKCiQ2WxWVlaWSktLFRUVpaSkJJ04caLZ8bt27dKUKVM0ffp0ffHFF5o0aZImTZqk/fv3240bP368jh8/btvef/99u+enTp2qr776Slu3btXmzZv1ySef6Omnn3a0fQAA0Ak5HHhycnKUnp6utLQ0DR06VHl5eerWrZvWrl3b7PgVK1Zo/Pjxev755zVkyBC9/PLLGjlypFatWmU3ztPTUyEhIbatZ8+etue+/vprFRYW6u2331ZcXJzuvPNOrVy5UuvXr9exY8ccnQIAAOhkHAo89fX1KikpUWJi4q8F3N2VmJio4uLiZo8pLi62Gy9JSUlJl43fsWOHgoKCNGjQIP3ud7/TTz/9ZFfD399fo0aNsu1LTEyUu7u79uzZ48gUAABAJ+TQhQdPnTqlxsZGBQcH2+0PDg7WwYMHmz3GYrE0O95isdgejx8/Xg8//LD69u2rI0eOaP78+br//vtVXFwsDw8PWSwWBQUF2TfepYsCAgLs6vy9uro61dXV2R7X1NQ4MlUAAGAg7eJKy5MnT7b9PGzYMA0fPlz9+/fXjh07NG7cuBbVzM7O1uLFi53VIgAA6MAc+kgrMDBQHh4eqqystNtfWVmpkJCQZo8JCQlxaLwk9evXT4GBgTp8+LCtxj8uir548aJ+/vnnK9bJzMxUdXW1bTt69Og15wcAAIzJocBjMpkUExOjoqIi276mpiYVFRUpPj6+2WPi4+PtxkvS1q1brzhekn788Uf99NNP6tOnj61GVVWVSkpKbGO2b9+upqYmxcXFNVvD09NTvr6+dhsAAOicHP6Wltls1h//+Ee9++67+vrrr/W73/1OtbW1SktLkyRNmzZNmZmZtvGzZ89WYWGhli9froMHD2rRokXat2+fMjIyJElnz57V888/r927d+v7779XUVGRfvOb32jAgAFKSkqSJA0ZMkTjx49Xenq69u7dq88//1wZGRmaPHmyQkNDnfF7AAAABubwGp6UlBSdPHlSCxculMViUXR0tAoLC20Lk8vLy+Xu/muOGjNmjPLz87VgwQLNnz9fAwcO1KZNmxQZGSlJ8vDw0Jdffql3331XVVVVCg0N1X333aeXX35Znp6etjrvvfeeMjIyNG7cOLm7uys5OVmvv/76jc4fAAB0Ai1atJyRkWE7Q/OPduzYcdm+Rx55RI888kiz4729vfWXv/zlmq8ZEBCg/Px8h/oEAACQuJcWAADoBAg8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Ag8AADA8Fp0awkAgHMcPnHWJXV7djcpzN/bJbWBjojAAwBtoGd3k7y7emhOQZlL6nt39dC25xIIPcD/R+ABgDYQ5u+tbc8l6HRtvdNrHz5xVnMKynS6tp7AA/x/BB4AaCNh/t4EEqCVEHjgdBVV5132f60AALQEgQdOVVF1XonLd+p8Q6NL6nt39VDP7iaX1AYAGBeBB051urZe5xsalZsSrQFBPZxen2+eAABagsADlxgQ1EORYX5t3QYAAJK48CAAAOgECDwAAMDwCDwAAMDwCDwAAMDwWLQM/H+uuM4P1w4CgPaBwINOrzXuacS1gwCgbRF40Om58p5GEtcOAoD2gMADiHsaAYDRsWgZAAAYHoEHAAAYHoEHAAAYHoEHAAAYHoEHAAAYHoEHAAAYHoEHAAAYHoEHAAAYHoEHAAAYHldaBgCDcsXNa7lVCjoqAg8AGIwrb4jr3dVD255LIPSgw2lR4Fm9erWWLl0qi8WiqKgorVy5UrGxsVccv2HDBr344ov6/vvvNXDgQL3yyiuaMGGCJKmhoUELFizQli1b9O2338rPz0+JiYlasmSJQkNDbTUiIiL0ww8/2NXNzs7WvHnzWjIFADAsV90Q9/CJs5pTUKbTtfUEHnQ4DgeegoICmc1m5eXlKS4uTrm5uUpKStKhQ4cUFBR02fhdu3ZpypQpys7O1gMPPKD8/HxNmjRJpaWlioyM1Llz51RaWqoXX3xRUVFROn36tGbPnq1//ud/1r59++xqvfTSS0pPT7c99vHxacGUAcD4uCEuYM/hRcs5OTlKT09XWlqahg4dqry8PHXr1k1r165tdvyKFSs0fvx4Pf/88xoyZIhefvlljRw5UqtWrZIk+fn5aevWrXr00Uc1aNAg3X777Vq1apVKSkpUXl5uV8vHx0chISG2rXv37i2YMgAA6GwcCjz19fUqKSlRYmLirwXc3ZWYmKji4uJmjykuLrYbL0lJSUlXHC9J1dXVcnNzk7+/v93+JUuWqFevXhoxYoSWLl2qixcvXrFGXV2dampq7DYAANA5OfSR1qlTp9TY2Kjg4GC7/cHBwTp48GCzx1gslmbHWyyWZsdfuHBBf/jDHzRlyhT5+vra9j/77LMaOXKkAgICtGvXLmVmZur48ePKyclptk52drYWL17syPQAAIBBtatvaTU0NOjRRx+V1WrVm2++afec2Wy2/Tx8+HCZTCY988wzys7Olqen52W1MjMz7Y6pqalReHi465oHAADtlkOBJzAwUB4eHqqsrLTbX1lZqZCQkGaPCQkJua7xl8LODz/8oO3bt9ud3WlOXFycLl68qO+//16DBg267HlPT89mgxAAAOh8HFrDYzKZFBMTo6KiItu+pqYmFRUVKT4+vtlj4uPj7cZL0tatW+3GXwo733zzjbZt26ZevXpds5eysjK5u7s3+80wAACAv+fwR1pms1mpqakaNWqUYmNjlZubq9raWqWlpUmSpk2bprCwMGVnZ0uSZs+erYSEBC1fvlwTJ07U+vXrtW/fPr311luSfgk7//Iv/6LS0lJt3rxZjY2NtvU9AQEBMplMKi4u1p49e3T33XfLx8dHxcXFmjt3rh5//HH17NnTWb8LAABgUA4HnpSUFJ08eVILFy6UxWJRdHS0CgsLbQuTy8vL5e7+64mjMWPGKD8/XwsWLND8+fM1cOBAbdq0SZGRkZKkiooK/e///q8kKTo62u61/vrXv2rs2LHy9PTU+vXrtWjRItXV1alv376aO3eu3RodAACAK3GzWq3Wtm6iNdTU1MjPz0/V1dXXXB+ElttfUa0HVn6mzbPuVGSYX1u3A8CJ+O8bbcFZ79/cLR0AABgegQcAABgegQcAABgegQcAABgegQcAABgegQcAABgegQcAABgegQcAABheu7pbOlpXRdV5na6td2rNwyfOOrUeAADOQODppCqqzitx+U6db2h0em3vrh7q2d3k9LoAALQUgaeTOl1br/MNjcpNidaAoB5Ord2zu0lh/t5OrQkAwI0g8HRyA4J6cE8cAIDhsWgZAAAYHoEHAAAYHoEHAAAYHoEHAAAYHouWAQAOcdX1tviGJ1yJwAMAuC49u5vk3dVDcwrKXFLfu6uHtj2XQOiBSxB4AADXJczfW9ueS3D6FdqlX84azSko0+naegIPXILAAwC4bmH+3gQSdEgsWgYAAIZH4AEAAIbHR1oAAEOrqDrvknVHEt8s60gIPAAAw6qoOq/E5Tt1vqHRJfX5ZlnHQeABABjW6dp6nW9oVG5KtAYE9XBqbb5Z1rEQeNo5V52KddWFwwCgPRoQ1EORYX5t3QbaEIGnHWuNU7E9u5tcUhsAgPaEwOMkrjgTc/jEWZedipVYbAeg/XH22efWOJvNrTY6BgKPE7jyTIx3Vw+N7hvAH3oAhubK21a46mw2t9roWAg8TuDKRXEkfACdgStvW+Gqv0e51UbHQuBxIhbFAUDLdcTbVnTEnjsrrrQMAAAMj8ADAAAMj8ADAAAMj8ADAAAMj0XLAAC0U664xk9n/fYvgQcAgHbG1dcl6ozX92lR4Fm9erWWLl0qi8WiqKgorVy5UrGxsVccv2HDBr344ov6/vvvNXDgQL3yyiuaMGGC7Xmr1aqsrCz98Y9/VFVVle644w69+eabGjhwoG3Mzz//rFmzZunDDz+Uu7u7kpOTtWLFCvXo4fwrEAMA0JZcdY2fS9f3+dt3P+t0J7uCv8OBp6CgQGazWXl5eYqLi1Nubq6SkpJ06NAhBQUFXTZ+165dmjJlirKzs/XAAw8oPz9fkyZNUmlpqSIjIyVJr776ql5//XW9++676tu3r1588UUlJSXpwIED8vLykiRNnTpVx48f19atW9XQ0KC0tDQ9/fTTys/Pv8FfAQAA7Y8rrvHTma8O7Wa1Wq2OHBAXF6fRo0dr1apVkqSmpiaFh4dr1qxZmjdv3mXjU1JSVFtbq82bN9v23X777YqOjlZeXp6sVqtCQ0P13HPP6fe//70kqbq6WsHBwXrnnXc0efJkff311xo6dKj+9re/adSoUZKkwsJCTZgwQT/++KNCQ0Ov2XdNTY38/PxUXV0tX19fR6Z8TfsrqvXAys+0edadXHgQANCuueLej9KvZ4+c/V7orPdvh87w1NfXq6SkRJmZmbZ97u7uSkxMVHFxcbPHFBcXy2w22+1LSkrSpk2bJEnfffedLBaLEhMTbc/7+fkpLi5OxcXFmjx5soqLi+Xv728LO5KUmJgod3d37dmzRw899NBlr1tXV6e6ujrb4+rqakm//OKc7eyZGjXVndPZMzWqqXFzen0AAJzFx13y8XH+e9XZM00ueS+89L7t4PmZyzgUeE6dOqXGxkYFBwfb7Q8ODtbBgwebPcZisTQ73mKx2J6/tO9qY/7x47IuXbooICDANuYfZWdna/HixZftDw8Pv9L0blh8rstKAwDQIbjqvfDMmTPy82v5mSPDfksrMzPT7sxSU1OTfv75Z/Xq1Utubh3jLExNTY3Cw8N19OhRp38M1x4wv46N+XVszK9j60zz8/Hx0ZkzZ65r+crVOBR4AgMD5eHhocrKSrv9lZWVCgkJafaYkJCQq46/9M/Kykr16dPHbkx0dLRtzIkTJ+xqXLx4UT///PMVX9fT01Oenp52+/z9/a8+wXbK19fXkH+gL2F+HRvz69iYX8fWWeZ3I2d2LnHoSssmk0kxMTEqKiqy7WtqalJRUZHi4+ObPSY+Pt5uvCRt3brVNr5v374KCQmxG1NTU6M9e/bYxsTHx6uqqkolJSW2Mdu3b1dTU5Pi4uIcmQIAAOiEHP5Iy2w2KzU1VaNGjVJsbKxyc3NVW1urtLQ0SdK0adMUFham7OxsSdLs2bOVkJCg5cuXa+LEiVq/fr327dunt956S5Lk5uamOXPm6N///d81cOBA29fSQ0NDNWnSJEnSkCFDNH78eKWnpysvL08NDQ3KyMjQ5MmTb/gUFwAAMD6HA09KSopOnjyphQsXymKxKDo6WoWFhbZFx+Xl5XJ3//XE0ZgxY5Sfn68FCxZo/vz5GjhwoDZt2mS7Bo8kvfDCC6qtrdXTTz+tqqoq3XnnnSosLLRdg0eS3nvvPWVkZGjcuHG2Cw++/vrrNzL3ds/T01NZWVmXfTRnFMyvY2N+HRvz69iYn+Mcvg4PAABAR8Pd0gEAgOEReAAAgOEReAAAgOEReAAAgOEReNrY6tWrFRERIS8vL8XFxWnv3r1XHDt27Fi5ubldtk2cOLEVO3aMI/OTpNzcXA0aNEje3t4KDw/X3LlzdeHChVbq1nGOzK+hoUEvvfSS+vfvLy8vL0VFRamwsLAVu3XMJ598ogcffFChoaFyc3Oz3f/uanbs2KGRI0fK09NTAwYM0DvvvOPyPlvC0bkdP35cjz32mG699Va5u7trzpw5rdJnSzk6v40bN+ree+9V79695evrq/j4eP3lL39pnWZbwNH5ffbZZ7rjjjvUq1cveXt7a/DgwXrttddap9kWaMl/e5d8/vnn6tKli+3Cve2Ro/PbsWNHs+99V7q11JUQeNpQQUGBzGazsrKyVFpaqqioKCUlJV12VelLNm7cqOPHj9u2/fv3y8PDQ4888kgrd359HJ1ffn6+5s2bp6ysLH399ddas2aNCgoKNH/+/Fbu/Po4Or8FCxboP//zP7Vy5UodOHBAM2bM0EMPPaQvvviilTu/PrW1tYqKitLq1auva/x3332niRMn6u6771ZZWZnmzJmjp556ql2+cTo6t7q6OvXu3VsLFixQVFSUi7u7cY7O75NPPtG9996rLVu2qKSkRHfffbcefPBBw/zZ7N69uzIyMvTJJ5/o66+/1oIFC7RgwQLb9eDaG0fnd0lVVZWmTZumcePGuagz52jp/A4dOmT3HviP99i8JivaTGxsrHXmzJm2x42NjdbQ0FBrdnb2dR3/2muvWX18fKxnz551VYs3xNH5zZw503rPPffY7TObzdY77rjDpX22lKPz69Onj3XVqlV2+x5++GHr1KlTXdqnM0iy/ulPf7rqmBdeeMF622232e1LSUmxJiUlubCzG3c9c/t7CQkJ1tmzZ7usH2dzdH6XDB061Lp48WLnN+RkLZ3fQw89ZH388ced35CTOTK/lJQU64IFC6xZWVnWqKgol/blLNczv7/+9a9WSdbTp0/f0GtxhqeN1NfXq6SkRImJibZ97u7uSkxMVHFx8XXVWLNmjSZPnqzu3bu7qs0Wa8n8xowZo5KSEtvHQt9++622bNmiCRMmtErPjmjJ/Orq6uwupilJ3t7e+uyzz1zaa2spLi62+31IUlJS0nX/eUb70dTUpDNnziggIKCtW3GJL774Qrt27VJCQkJbt+I069at07fffqusrKy2bsVloqOj1adPH9177736/PPPHT7esHdLb+9OnTqlxsZG2xWqLwkODtbBgwevefzevXu1f/9+rVmzxlUt3pCWzO+xxx7TqVOndOedd8pqterixYuaMWNGu/xIqyXzS0pKUk5Oju666y71799fRUVF2rhxoxobG1ujZZezWCzN/j5qamp0/vx5eXt7t1FncNSyZct09uxZPfroo23dilPddNNNOnnypC5evKhFixbpqaeeauuWnOKbb77RvHnz9Omnn6pLF+O9rffp00d5eXkaNWqU6urq9Pbbb2vs2LHas2ePRo4ced11OMPTQa1Zs0bDhg1TbGxsW7fiNDt27NB//Md/6I033lBpaak2btyojz76SC+//HJbt+YUK1as0MCBAzV48GCZTCZlZGQoLS3N7lYsQFvLz8/X4sWL9cEHHzi+RqKd+/TTT7Vv3z7l5eUpNzdX77//flu3dMMaGxv12GOPafHixbr11lvbuh2XGDRokJ555hnFxMRozJgxWrt2rcaMGePwwnPjRcEOIjAwUB4eHqqsrLTbX1lZqZCQkKseW1tbq/Xr1+ull15yZYs3pCXze/HFF/Xb3/7W9n9dw4YNs91j7d/+7d/aVTBoyfx69+6tTZs26cKFC/rpp58UGhqqefPmqV+/fq3RssuFhIQ0+/vw9fXl7E4HsX79ej311FPasGHDZR9PGkHfvn0l/fJ3S2VlpRYtWqQpU6a0cVc35syZM9q3b5+++OILZWRkSPrlI0mr1aouXbro448/1j333NPGXTpfbGysw8sB2s87SCdjMpkUExOjoqIi276mpiYVFRUpPj7+qsdu2LBBdXV1evzxx13dZou1ZH7nzp27LNR4eHhIkqzt7JZvN/Lvz8vLS2FhYbp48aL+53/+R7/5zW9c3W6riI+Pt/t9SNLWrVuv+ftA+/D+++8rLS1N77//fru+1IWzNDU1qa6urq3buGG+vr76v//7P5WVldm2GTNmaNCgQSorK1NcXFxbt+gSZWVl6tOnj0PHcIanDZnNZqWmpmrUqFGKjY1Vbm6uamtrlZaWJkmaNm2awsLClJ2dbXfcmjVrNGnSJPXq1ast2r5ujs7vwQcfVE5OjkaMGKG4uDgdPnxYL774oh588EFb8GlPHJ3fnj17VFFRoejoaFVUVGjRokVqamrSCy+80JbTuKKzZ8/q8OHDtsffffedysrKFBAQoJtvvlmZmZmqqKjQf/3Xf0mSZsyYoVWrVumFF17Qk08+qe3bt+uDDz7QRx991FZTuCJH5yb98hfspWNPnjypsrIymUwmDR06tLXbvyZH55efn6/U1FStWLFCcXFxtuubeHt7y8/Pr03mcDWOzm/16tW6+eabNXjwYEm/fA1/2bJlevbZZ9uk/2txZH7u7u6KjIy0Oz4oKEheXl6X7W8vHP33l5ubq759++q2227ThQsX9Pbbb2v79u36+OOPHXvhG/qOF27YypUrrTfffLPVZDJZY2Njrbt377Y9l5CQYE1NTbUbf/DgQask68cff9zKnbaMI/NraGiwLlq0yNq/f3+rl5eXNTw83Pqv//qvN/xVRFdyZH47duywDhkyxOrp6Wnt1auX9be//a21oqKiDbq+Ppe+CvqP26U5paamWhMSEi47Jjo62moymaz9+vWzrlu3rtX7vh4tmVtz42+55ZZW7/16ODq/hISEq45vbxyd3+uvv2697bbbrN26dbP6+vpaR4wYYX3jjTesjY2NbTOBa2jJn8+/196/lu7o/F555RXb+0JAQIB17Nix1u3btzv8um5Wazv7rAAAAMDJWMMDAAAMj8ADAAAMj8ADAAAMj8ADAAAMj8ADAAAMj8ADAAAMj8ADAAAMj8ADAAAMj8ADAAAMj8ADAAAMj8ADAAAMj8ADAAAM7/8B+S1bdJNmetIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 10 time is now 0\n",
      "working on HD 1176 time is now 6\n",
      "working on HD 2238 time is now 11\n",
      "working on HD 3365 time is now 16\n",
      "working on HD 4567 time is now 21\n",
      "out of 4207 total HDs, there were 4175.0 contiguous and 4207.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 32\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 32 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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YU6yoqMhIMkVFRVXmlZaWms2bN5vS0tL/TtyzwZiMyCP/ngo+9nfo0CETFhZmVq5c6TV9xIgRZtiwYcYYYz799FMjySxatMgzf/78+UaS57UmJSWZ1NTUY/bTrl0785e//OWY8+fOnWuaNGniGc/JyTHh4eGmpKTEGHNku4eEhJgFCxYYY4x56623TMeOHU1lZaVnGbfbbUJDQ83HH398wtedkZFhevToccJ2xhgjydx9991e0/r162fuueceY4wxr732momJiTHFxcWe+fPnzzeNGjUy+/btM8YYM3z4cBMbG+t5PcYYM336dBMeHm4qKipqVIdt1f6+Wpa3u9C0m/CBydtdWGd92EbNp4Y/1gz/drzP71/jiMVJ2rZtmw4ePKhLL71U4eHhnuHNN9/U9u3bvdp2797d83OrVq0kSQUFBZKk3NxcDRo0qMb9Llq0SIMGDVJcXJwiIiJ0yy236Oeff/ac5rjiiisUHBys999/X5L0r3/9S5GRkUpOTpYkbdy4Udu2bVNERISn5tjYWB06dKhK3TYkJSVVGT96xGLLli3q0aOHGjdu7Jnfv39/VVZWep0u6tGjh8LCwrzWUVxcrF27dlmvFwBQOw3/4s0G7uh1APPnz1dcXJzXPKfT6TUeHBzs+TkgIEDSkeszJCk0tOaPhf/222911VVX6Z577tFTTz2l2NhYff755xoxYoTKysoUFhYmh8OhG264QbNmzdLQoUM1a9Ys3XzzzQoKCvLUfd555+ntt9+usv5mzZrVuBYAAH6NIxYnqUuXLnI6nfr++++VmJjoNcTH1/xbJd27d9fixYtr1Hb9+vWqrKzUlClTdP755+vss8/WDz/8UKVdamqqPvroI23atElLlixRamqqZ16vXr30zTffqHnz5lXqjoqKqnHdNbVq1aoq4507d5Ykde7cWRs3blRJSYln/ooVK9SoUSN17NjRM23jxo0qLS31Wkd4eLhP2xkAULcIFicpIiJCDz74oO6//3698cYb2r59u7788ktNmzZNb7zxRo3Xk5GRodmzZysjI0NbtmxRXl6ennnmmWrbJiYmqry8XNOmTdOOHTv01ltv6ZVXXqnSbsCAAWrZsqVSU1OVkJCgfv36eealpqaqadOmuuaaa/TZZ59p586dWrp0qe677z7t3r37mHVu27ZNubm52rdvn0pLS5Wbm6vc3NwTXvA5d+5cvf766/r666+VkZGhNWvWeC7OTE1NVUhIiIYPH678/Hx9+umnuvfee3XLLbeoRYsWnnWUlZVpxIgR2rx5sz788ENlZGRozJgxatSIX2MAaCj851TIf75usP08+eSTatasmbKysrRjxw5FR0erV69eeuSRR2q8josvvlhz587Vk08+qUmTJikyMlIDBgyotm2PHj30/PPP65lnnlF6eroGDBigrKws3XrrrV7tAgICNGzYMD377LOaOHGi17ywsDAtX75cEyZM0HXXXacDBw4oLi5OgwYNUmRk5DHrvPPOO72+7dKzZ09J0s6dO3XWWWcdc7nMzExlZ2dr1KhRatWqlWbPnq0uXbp4avn44481duxY9enTR2FhYbr++uv1/PPPe61j0KBB6tChgwYMGCC3261hw4bp8ccfP2afAIBTL8AYY05lhy6XS1FRUSoqKqryAXbo0CHt3LlTCQkJCgkJOTKRO2/6vYCAAOXk5GjIkCG1Xsdtt92mwsJCzZs3z1pdJ6va31fL8vcU6appn+uDey9U1zj7p6jqAjWfGv5YM/zb8T6/f63hH7GIjj/yIX+GPysEAAB/0PCDhXTkQ54PegAAGjz/CBbwazbOts2cOfPkCwEA1DkupwcAANYQLAAAgDUNMlic4i+qALXC7ykAVNWggkVgYKAk8Shs+IWjz2X59a3aAeBM16Au3gwKClJYWJh++uknBQcHc0dFNEjGGB08eFAFBQWKjo72BGIAQAMLFgEBAWrVqpV27typ7777rr7LAY4rOjpaLVu2rO8yAKBBaVDBQpIcDoc6dOjA6RA0aMHBwRypAIBqNLhgIUmNGjWqs1skAwCAusNFDAAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAa3wKFo8//rgCAgK8hk6dOtVVbQAAwM8E+brAOeeco0WLFv13BUE+rwIAAJymfE4FQUFBatmyZV3UAgAA/JzPweKbb75R69atFRISoqSkJGVlZalt27bHbO92u+V2uz3jLperdpX6aE9hqfaXlJ2SvmyJaexQXHRofZcBAECt+RQs+vXrp5kzZ6pjx47au3evMjMzddFFFyk/P18RERHVLpOVlaXMzEwrxdbUnsJSJU9ZptLyilPa78kKDQ7UogcGEi4AAH7Lp2AxePBgz8/du3dXv3791K5dO/3zn//UiBEjql0mPT1daWlpnnGXy6X4+Phallsz+0vKVFpeoRduPleJzcPrtC9bthUUa9ycXO0vKSNYAAD81kldeRkdHa2zzz5b27ZtO2Ybp9Mpp9N5Mt3UWmLzcHWNi6qXvgEAOBOd1H0siouLtX37drVq1cpWPQAAwI/5FCwefPBBLVu2TN9++61Wrlypa6+9VoGBgRo2bFhd1QcAAPyIT6dCdu/erWHDhunnn39Ws2bNdOGFF2rVqlVq1qxZXdUHAAD8iE/BIjs7u67qAAAApwGeFQIAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMCakwoWkyZNUkBAgMaNG2epHAAA4M9qHSzWrl2rV199Vd27d7dZDwAA8GO1ChbFxcVKTU3V3/72N8XExNiuCQAA+KlaBYvRo0fryiuvVHJysu16AACAHwvydYHs7Gx9+eWXWrt2bY3au91uud1uz7jL5fK1SwDAMWwrKK7vEmosprFDcdGh9V0G6phPwWLXrl0aO3asFi5cqJCQkBotk5WVpczMzFoVBwCoXkxjh0KDAzVuTm59l1JjocGBWvTAQMLFac6nYLF+/XoVFBSoV69enmkVFRVavny5XnrpJbndbgUGBnotk56errS0NM+4y+VSfHz8SZYNAGe2uOhQLXpgoPaXlNV3KTWyraBY4+bkan9JGcHiNOdTsBg0aJDy8vK8pt1+++3q1KmTJkyYUCVUSJLT6ZTT6Ty5KgEAVcRFh/IhjQbHp2ARERGhrl27ek1r3LixmjRpUmU6AAA483DnTQAAYI3P3wr5raVLl1ooAwAAnA44YgEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALDGp2Axffp0de/eXZGRkYqMjFRSUpIWLFhQV7UBAAA/41OwaNOmjSZNmqT169dr3bp1+v3vf69rrrlGmzZtqqv6AACAHwnypfHVV1/tNf7UU09p+vTpWrVqlc455xyrhQEAAP/jU7D4tYqKCs2dO1clJSVKSko6Zju32y232+0Zd7lcte0SOGl7Cku1v6SsvsuosW0FxfVdQq35U+3+VCtOLX/7myFJMY0diosOrbf+fQ4WeXl5SkpK0qFDhxQeHq6cnBx16dLlmO2zsrKUmZl5UkUCNuwpLFXylGUqLa+o71J8EhocqJjGjvouo8ZiGjsUGhyocXNy67sUn/jbdkbd8+e/GYseGFhv4cLnYNGxY0fl5uaqqKhI77zzjoYPH65ly5YdM1ykp6crLS3NM+5yuRQfH1/7ioFa2l9SptLyCr1w87lKbB5e3+XUWH3/78NXcdGhWvTAQP6XB7/nj38zthUUa9ycXO0vKfOfYOFwOJSYmChJOu+887R27Vq9+OKLevXVV6tt73Q65XQ6T65KwKLE5uHqGhdV32Wc1uKiQ/mQxmmDvxm+Oen7WFRWVnpdQwEAAM5cPh2xSE9P1+DBg9W2bVsdOHBAs2bN0tKlS/Xxxx/XVX0AAMCP+BQsCgoKdOutt2rv3r2KiopS9+7d9fHHH+vSSy+tq/oAAIAf8SlY/OMf/6irOgAAwGmAZ4UAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACs8SlYZGVlqU+fPoqIiFDz5s01ZMgQbd26ta5qAwAAfsanYLFs2TKNHj1aq1at0sKFC1VeXq7LLrtMJSUldVUfAADwI0G+NP7oo4+8xmfOnKnmzZtr/fr1GjBggNXCAACA//EpWPxWUVGRJCk2NvaYbdxut9xut2fc5XKdTJcAAKABq/XFm5WVlRo3bpz69++vrl27HrNdVlaWoqKiPEN8fHxtuwQAAA1crYPF6NGjlZ+fr+zs7OO2S09PV1FRkWfYtWtXbbsEAAANXK1OhYwZM0YffPCBli9frjZt2hy3rdPplNPprFVxAADAv/gULIwxuvfee5WTk6OlS5cqISGhruoCAAB+yKdgMXr0aM2aNUvvvfeeIiIitG/fPklSVFSUQkND66RAAADgP3y6xmL69OkqKirSxRdfrFatWnmGOXPm1FV9AADAj/h8KgQAAOBYeFYIAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGsIFgAAwBqCBQAAsIZgAQAArCFYAAAAa3wOFsuXL9fVV1+t1q1bKyAgQPPmzauDsgAAgD/yOViUlJSoR48eevnll+uiHgAA4MeCfF1g8ODBGjx4cF3UAgAA/BzXWAAAAGt8PmLhK7fbLbfb7Rl3uVx13aVf21ZQXN8l1FhMY4fiokPruwwAfsSf/sb5U60NSZ0Hi6ysLGVmZtZ1N34vprFDocGBGjcnt75LqbHQ4EAtemAg4QLACfnj3zjpyN+5mMaO+i7Dr9R5sEhPT1daWppn3OVyKT4+vq679Ttx0aFa9MBA7S8pq+9SamRbQbHGzcnV/pIyggWAE/K3v3FHcWTWd3UeLJxOp5xOZ113c1qIiw7lFxjAaYu/cWcGn4NFcXGxtm3b5hnfuXOncnNzFRsbq7Zt21otDgAA+Befg8W6det0ySWXeMaPnuYYPny4Zs6caa0wAADgf3wOFhdffLGMMXVRCwAA8HPcxwIAAFhDsAAAANYQLAAAgDUECwAAYA3BAgAAWEOwAAAA1hAsAACANQQLAABgDcECAABYQ7AAAADWECwAAIA1BAsAAGANwQIAAFhDsAAAANYQLAAAgDUECwAAYA3BAgAAWEOwAAAA1hAsAACANQQLAABgDcECAABYQ7AAAADWECwAAIA1BAsAAGANwQIAAFhDsAAAANYQLAAAgDUECwAAYA3BAgAAWEOwAAAA1hAsAACANQQLAABgDcECAABYQ7AAAADWECwAAIA1BAsAAGANwQIAAFhDsAAAANYQLAAAgDUECwAAYA3BAgAAWEOwAAAA1hAsAACANQQLAABgDcECAABYQ7AAAADWECwAAIA1BAsAAGBNrYLFyy+/rLPOOkshISHq16+f1qxZY7suAADgh3wOFnPmzFFaWpoyMjL05ZdfqkePHkpJSVFBQUFd1AcAAPyIz8Hi+eef18iRI3X77berS5cueuWVVxQWFqbXX3+9LuoDAAB+JMiXxmVlZVq/fr3S09M90xo1aqTk5GR98cUX1S7jdrvldrs940VFRZIkl8tVm3prpPiAS5Xugyo+4JLLFVBn/ZzJjm7jr3bsVfGButuXNu34qYTfCwCntbr8/Dv6uW2MOX5D44M9e/YYSWblypVe08ePH2/69u1b7TIZGRlGEgMDAwMDA8NpMOzateu4WcGnIxa1kZ6errS0NM94ZWWlfvnlFzVp0kQBAfbSlMvlUnx8vHbt2qXIyEhr60XdYH/5F/aX/2Bf+Rd/2l/GGB04cECtW7c+bjufgkXTpk0VGBioH3/80Wv6jz/+qJYtW1a7jNPplNPp9JoWHR3tS7c+iYyMbPA7B//F/vIv7C//wb7yL/6yv6Kiok7YxqeLNx0Oh8477zwtXrzYM62yslKLFy9WUlKS7xUCAIDTis+nQtLS0jR8+HD17t1bffv21QsvvKCSkhLdfvvtdVEfAADwIz4Hi5tvvlk//fSTJk6cqH379uncc8/VRx99pBYtWtRFfTXmdDqVkZFR5bQLGib2l39hf/kP9pV/OR33V4A54fdGAAAAaoZnhQAAAGsIFgAAwBqCBQAAsIZgAQAArDltggWPcrdr+fLluvrqq9W6dWsFBARo3rx5XvONMZo4caJatWql0NBQJScn65tvvvFq88svvyg1NVWRkZGKjo7WiBEjVFxc7NXmq6++0kUXXaSQkBDFx8fr2WefrVLL3Llz1alTJ4WEhKhbt2768MMPfa7ldJaVlaU+ffooIiJCzZs315AhQ7R161avNocOHdLo0aPVpEkThYeH6/rrr69yo7vvv/9eV155pcLCwtS8eXONHz9ehw8f9mqzdOlS9erVS06nU4mJiZo5c2aVek70XqxJLaez6dOnq3v37p4bIiUlJWnBggWe+eyrhm3SpEkKCAjQuHHjPNPYZ7/hy7NCGqrs7GzjcDjM66+/bjZt2mRGjhxpoqOjzY8//ljfpfmtDz/80Dz66KPm3XffNZJMTk6O1/xJkyaZqKgoM2/ePLNx40bzhz/8wSQkJJjS0lJPm8svv9z06NHDrFq1ynz22WcmMTHRDBs2zDO/qKjItGjRwqSmppr8/Hwze/ZsExoaal599VVPmxUrVpjAwEDz7LPPms2bN5s//elPJjg42OTl5flUy+ksJSXFzJgxw+Tn55vc3FxzxRVXmLZt25ri4mJPm7vvvtvEx8ebxYsXm3Xr1pnzzz/fXHDBBZ75hw8fNl27djXJyclmw4YN5sMPPzRNmzY16enpnjY7duwwYWFhJi0tzWzevNlMmzbNBAYGmo8++sjTpibvxRPVcrp7//33zfz5883XX39ttm7dah555BETHBxs8vPzjTHsq4ZszZo15qyzzjLdu3c3Y8eO9Uxnn3k7LYJF3759zejRoz3jFRUVpnXr1iYrK6seqzp9/DZYVFZWmpYtW5rnnnvOM62wsNA4nU4ze/ZsY4wxmzdvNpLM2rVrPW0WLFhgAgICzJ49e4wxxvz1r381MTExxu12e9pMmDDBdOzY0TN+0003mSuvvNKrnn79+pk//vGPNa7lTFNQUGAkmWXLlhljjmyP4OBgM3fuXE+bLVu2GEnmiy++MMYcCZKNGjUy+/bt87SZPn26iYyM9Oyfhx56yJxzzjlefd18880mJSXFM36i92JNajkTxcTEmL///e/sqwbswIEDpkOHDmbhwoVm4MCBnmDBPqvK70+FHH2Ue3JysmfaiR7ljpOzc+dO7du3z2ubR0VFqV+/fp5t/sUXXyg6Olq9e/f2tElOTlajRo20evVqT5sBAwbI4XB42qSkpGjr1q3av3+/p82v+zna5mg/NanlTFNUVCRJio2NlSStX79e5eXlXtuoU6dOatu2rdf+6tatm9eN7lJSUuRyubRp0yZPm+Pti5q8F2tSy5mkoqJC2dnZKikpUVJSEvuqARs9erSuvPLKKtuVfVZVnT/dtK795z//UUVFRZU7f7Zo0UL/93//V09Vnd727dsnSdVu86Pz9u3bp+bNm3vNDwoKUmxsrFebhISEKus4Oi8mJkb79u07YT8nquVMUllZqXHjxql///7q2rWrpCPbyOFwVHn432+3Y3Xb8Oi847VxuVwqLS3V/v37T/herEktZ4K8vDwlJSXp0KFDCg8PV05Ojrp06aLc3Fz2VQOUnZ2tL7/8UmvXrq0yj/dXVX4fLAD81+jRo5Wfn6/PP/+8vkvBcXTs2FG5ubkqKirSO++8o+HDh2vZsmX1XRaqsWvXLo0dO1YLFy5USEhIfZfjF/z+VEhtHuWOk3N0ux5vm7ds2VIFBQVe8w8fPqxffvnFq0116/h1H8dq8+v5J6rlTDFmzBh98MEH+vTTT9WmTRvP9JYtW6qsrEyFhYVe7X+7HWu7LyIjIxUaGlqj92JNajkTOBwOJSYm6rzzzlNWVpZ69OihF198kX3VAK1fv14FBQXq1auXgoKCFBQUpGXLlmnq1KkKCgpSixYt2Ge/4ffBgke5n3oJCQlq2bKl1zZ3uVxavXq1Z5snJSWpsLBQ69ev97RZsmSJKisr1a9fP0+b5cuXq7y83NNm4cKF6tixo2JiYjxtft3P0TZH+6lJLac7Y4zGjBmjnJwcLVmypMrppfPOO0/BwcFe22jr1q36/vvvvfZXXl6eVxhcuHChIiMj1aVLF0+b4+2LmrwXa1LLmaiyslJut5t91QANGjRIeXl5ys3N9Qy9e/dWamqq52f22W+csstE61B2drZxOp1m5syZZvPmzeauu+4y0dHRXlfgwjcHDhwwGzZsMBs2bDCSzPPPP282bNhgvvvuO2PMka94RkdHm/fee8989dVX5pprrqn266Y9e/Y0q1evNp9//rnp0KGD19dNCwsLTYsWLcwtt9xi8vPzTXZ2tgkLC6vyddOgoCAzefJks2XLFpORkVHt101PVMvp7J577jFRUVFm6dKlZu/evZ7h4MGDnjZ33323adu2rVmyZIlZt26dSUpKMklJSZ75R78Od9lll5nc3Fzz0UcfmWbNmlX7dbjx48ebLVu2mJdffrnar8Od6L14olpOdw8//LBZtmyZ2blzp/nqq6/Mww8/bAICAswnn3xijGFf+YNffyvEGPbZb50WwcIYY6ZNm2batm1rHA6H6du3r1m1alV9l+TXPv30UyOpyjB8+HBjzJGveT722GOmRYsWxul0mkGDBpmtW7d6rePnn382w4YNM+Hh4SYyMtLcfvvt5sCBA15tNm7caC688ELjdDpNXFycmTRpUpVa/vnPf5qzzz7bOBwOc84555j58+d7za9JLaez6vaTJDNjxgxPm9LSUjNq1CgTExNjwsLCzLXXXmv27t3rtZ5vv/3WDB482ISGhpqmTZuaBx54wJSXl3u1+fTTT825555rHA6Had++vVcfR53ovViTWk5nd9xxh2nXrp1xOBymWbNmZtCgQZ5QYQz7yh/8Nliwz7zx2HQAAGCN319jAQAAGg6CBQAAsIZgAQAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGYAEAAKwhWAAAAGv+HyWTIG1BHkHwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 736126 district pop vs 774870 target\n",
      "we'll also trim any HD with total islands' pop <= 15497\n",
      "working on HD 121\n",
      "Out of 32 discontig HDs 32 had discontig HDs.\n",
      "Of these, 2 won't be under 736126 after all minor discontigys were shed, while 30 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 30 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79 4 30\n",
      "{3200, 257, 258, 3203, 3204, 3205, 646, 3206, 648, 3461, 3464, 1931, 2575, 273, 1810, 3604, 3605, 2196, 1561, 1306, 1950, 3488, 2340, 1317, 691, 2356, 3254, 3257, 3262, 3264, 2626, 2243, 3147, 3148, 459, 3279, 3154, 3155, 3289, 1626, 3165, 3173, 102, 249, 3178, 363, 3434, 2160, 3574, 3063, 246, 3577, 3197}\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e16dfee-b5bc-4863-8bed-9a6b22290aaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes about 3sec per HD :-( The total number of HDs to fix is 30\n",
      "swell-generating HD 121 our 1 th HD we must regrow out of 30 0 sec elapsed\n",
      "swell-generating HD 2158 our 21 th HD we must regrow out of 30 1207 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 1.00749 0.12027\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00769 0.11834\n"
     ]
    },
    {
     "data": {
      "image/png": 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6AADAbJbCTHh4uCZPnqzKykpvm9vtVmVlpbKysvwqwN85IyIiFB0d7XMAAID+x/Kl2TabTfn5+UpLS1NGRoYcDodcLpcKCgokSXPnzlVCQoLsdrukr0/wPXDggPfX9fX1qqur0+DBgzV27NguzQkAANAZy2EmLy9Px48f14oVK+R0OpWamqqKigrvCbxHjhxRaOg3Gz7Hjh3TpEmTvI/Xrl2rtWvXKjs7W1VVVV2aEwAAoDN+3QG4sLBQhYWFHT53PqCcl5SUJI/Hc1lzAgAAdMbYq5kAAAAkwgwAADAcYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABhtQE8XAJis/tQ5nXS1Bnzew41nAj4nAPRVhBnAT/Wnziln3Ts619YelPmjBoZp2KDwoMwNAH0JYQbw00lXq861tcuRl6qxsYMDPv+wQeFKGBoV8HkBoK8hzACXaWzsYCUnxPR0GQDQb/l1AnBJSYmSkpIUGRmpzMxM7dmz56L9t27dqvHjxysyMlITJ07Ujh07fJ4/c+aMCgsLdfXVVysqKkoTJkxQaWmpP6UBAIB+xnKYKS8vl81m08qVK1VbW6uUlBTl5uaqsbGxw/67d+/WnDlz9OMf/1j79u3TrFmzNGvWLO3fv9/bx2azqaKiQv/+7/+ujz/+WEVFRSosLNRrr73m/8oAAEC/YDnMrF+/XvPmzVNBQYF3B+WKK67Q5s2bO+z/7LPP6vvf/74ee+wx3XDDDfrJT36im266SRs3bvT22b17t/Lz8zV9+nQlJSXp4YcfVkpKyiV3fAAAACyFmdbWVtXU1CgnJ+ebCUJDlZOTo+rq6g7HVFdX+/SXpNzcXJ/+U6ZM0Wuvvab6+np5PB69/fbb+uSTT/S9732v01paWlrU3NzscwAAgP7HUpg5ceKE2tvbFRcX59MeFxcnp9PZ4Rin03nJ/hs2bNCECRN09dVXKzw8XN///vdVUlKiW265pdNa7Ha7YmJivEdiYqKVpQAAgD6iV9wBeMOGDfrggw/02muvqaamRuvWrdOCBQu0a9euTscUFxerqanJexw9erQbKwYAAL2FpUuzR4wYobCwMDU0NPi0NzQ0KD4+vsMx8fHxF+1/7tw5LV26VK+++qpmzJghSfr2t7+turo6rV279oKPqM6LiIhQRESElfIBAEAfZGlnJjw8XJMnT1ZlZaW3ze12q7KyUllZWR2OycrK8ukvSTt37vT2b2trU1tbm0JDfUsJCwuT2+22Uh4AAOiHLN80z2azKT8/X2lpacrIyJDD4ZDL5VJBQYEkae7cuUpISJDdbpckLVq0SNnZ2Vq3bp1mzJihLVu2aO/evdq0aZMkKTo6WtnZ2XrssccUFRWla6+9Vu+8845+/etfa/369QFcKgAA6Issh5m8vDwdP35cK1askNPpVGpqqioqKrwn+R45csRnl2XKlCkqKyvTsmXLtHTpUo0bN07btm1TcnKyt8+WLVtUXFys++67T3/5y1907bXX6l/+5V80f/78ACwRAAD0ZSEej8fT00UEQnNzs2JiYtTU1KTo6OieLgf9wP76Js3c8L62L5zG1xkYij9DoOcF4v27V1zNBAAA4C/CDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0fwKMyUlJUpKSlJkZKQyMzO1Z8+ei/bfunWrxo8fr8jISE2cOFE7duy4oM/HH3+sH/zgB4qJidGgQYOUnp6uI0eO+FMeAADoRyyHmfLyctlsNq1cuVK1tbVKSUlRbm6uGhsbO+y/e/duzZkzRz/+8Y+1b98+zZo1S7NmzdL+/fu9ff7nf/5H06ZN0/jx41VVVaU//OEPWr58uSIjI/1fGQAA6BdCPB6Px8qAzMxMpaena+PGjZIkt9utxMRELVy4UEuWLLmgf15enlwul7Zv3+5tu/nmm5WamqrS0lJJ0j333KOBAwfq3/7t3/xeSHNzs2JiYtTU1KTo6Gi/50HPqT91TiddrUGZe9igcCUMjQronPvrmzRzw/vavnCakhNiAjo3usf5P0NHXqrGxg4O+PzB+LkD+ppAvH8PsNK5tbVVNTU1Ki4u9raFhoYqJydH1dXVHY6prq6WzWbzacvNzdW2bdskfR2GXn/9dT3++OPKzc3Vvn37NHr0aBUXF2vWrFmd1tLS0qKWlhbv4+bmZitLQS9Tf+qccta9o3Nt7UGZP2pgmHYtzuaNBT6GDQpX1MAwFZXXBWV+fu6A7mEpzJw4cULt7e2Ki4vzaY+Li9PBgwc7HON0Ojvs73Q6JUmNjY06c+aMVq9erZ/+9Kdas2aNKioq9KMf/Uhvv/22srOzO5zXbrdr1apVVspHL3bS1apzbe1B+Rfy4cYzKiqv00lXK28q8JEwNEq7FmcHZUeQnzug+1gKM8HgdrslST/84Q/16KOPSpJSU1O1e/dulZaWdhpmiouLfXZ8mpublZiYGPyCEVRjYwfzkQ26VcLQKMIGYDhLYWbEiBEKCwtTQ0ODT3tDQ4Pi4+M7HBMfH3/R/iNGjNCAAQM0YcIEnz433HCD3n///U5riYiIUEREhJXyAQBAH2Tpaqbw8HBNnjxZlZWV3ja3263KykplZWV1OCYrK8unvyTt3LnT2z88PFzp6ek6dOiQT59PPvlE1157rZXyAABAP2T5Yyabzab8/HylpaUpIyNDDodDLpdLBQUFkqS5c+cqISFBdrtdkrRo0SJlZ2dr3bp1mjFjhrZs2aK9e/dq06ZN3jkfe+wx5eXl6ZZbbtGtt96qiooK/dd//ZeqqqoCs0oAANBnWQ4zeXl5On78uFasWCGn06nU1FRVVFR4T/I9cuSIQkO/2fCZMmWKysrKtGzZMi1dulTjxo3Ttm3blJyc7O1z5513qrS0VHa7XY888oiuv/56/fa3v9W0adMCsEQAANCX+XUCcGFhoQoLCzt8rqPdlNmzZ2v27NkXnfPBBx/Ugw8+6E85AACgH+O7mQAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMNqAni4A6C6HG8/06vkAAP4hzKDPGzYoXFEDw1RUXhfwuaMGhmnYoPCAzwsA6DrCDPq8hKFR2rU4WyddrQGfe9igcCUMjQr4vACArvPrnJmSkhIlJSUpMjJSmZmZ2rNnz0X7b926VePHj1dkZKQmTpyoHTt2dNp3/vz5CgkJkcPh8Kc0oEMJQ6OUnBAT8IMgAwA9z/LOTHl5uWw2m0pLS5WZmSmHw6Hc3FwdOnRIsbGxF/TfvXu35syZI7vdrpkzZ6qsrEyzZs1SbW2tkpOTffq++uqr+uCDDzRq1Cj/VwQAvUgwzq1iRxDwFeLxeDxWBmRmZio9PV0bN26UJLndbiUmJmrhwoVasmTJBf3z8vLkcrm0fft2b9vNN9+s1NRUlZaWetvq6+uVmZmpN954QzNmzFBRUZGKioq6XFdzc7NiYmLU1NSk6OhoK0tCL7C/vkkzN7yv7QunKTkhpqfLAS5b/alzyln3js61tQd87qiBYdq1OJtAgz4hEO/flnZmWltbVVNTo+LiYm9baGiocnJyVF1d3eGY6upq2Ww2n7bc3Fxt27bN+9jtduuBBx7QY489phtvvLFLtbS0tKilpcX7uLm52cJKACC4gnWu1uHGMyoqr9NJVythBvj/LIWZEydOqL29XXFxcT7tcXFxOnjwYIdjnE5nh/2dTqf38Zo1azRgwAA98sgjXa7Fbrdr1apVFqoHgO6VMDSKwAF0gx6/aV5NTY2effZZvfTSSwoJCenyuOLiYjU1NXmPo0ePBrFKAADQW1kKMyNGjFBYWJgaGhp82hsaGhQfH9/hmPj4+Iv2f++999TY2KhrrrlGAwYM0IABA/TFF19o8eLFSkpK6rSWiIgIRUdH+xwAAKD/sRRmwsPDNXnyZFVWVnrb3G63KisrlZWV1eGYrKwsn/6StHPnTm//Bx54QH/4wx9UV1fnPUaNGqXHHntMb7zxhtX1AACAfsbypdk2m035+flKS0tTRkaGHA6HXC6XCgoKJElz585VQkKC7Ha7JGnRokXKzs7WunXrNGPGDG3ZskV79+7Vpk2bJEnDhw/X8OHDfV5j4MCBio+P1/XXX3+56wMAAH2c5TCTl5en48ePa8WKFXI6nUpNTVVFRYX3JN8jR44oNPSbDZ8pU6aorKxMy5Yt09KlSzVu3Dht27btgnvMAAAA+MOvrzMoLCxUYWFhh89VVVVd0DZ79mzNnj27y/N//vnn/pQFAAD6oR6/mgkAAOByEGYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMNqCnCwAAWHe48UxQ5h02KFwJQ6OCMjcQLIQZADDIsEHhihoYpqLyuqDMHzUwTLsWZxNoYBTCDAAYJGFolHYtztZJV2vA5z7ceEZF5XU66WolzMAohBkAMEzC0CjCBvB/cAIwAAAwGjszsKz+1LmAb3EH62RGAEDfR5iBJfWnziln3Ts619Ye8LmjBoZp2KDwgM8LAOjb/AozJSUlevrpp+V0OpWSkqINGzYoIyOj0/5bt27V8uXL9fnnn2vcuHFas2aNbr/9dklSW1ubli1bph07dujTTz9VTEyMcnJytHr1ao0aNcq/VSFoTrpada6tXY68VI2NHRzQubkkFADgD8thpry8XDabTaWlpcrMzJTD4VBubq4OHTqk2NjYC/rv3r1bc+bMkd1u18yZM1VWVqZZs2aptrZWycnJOnv2rGpra7V8+XKlpKTo5MmTWrRokX7wgx9o7969AVkkAm9s7GAlJ8T0dBkAACjE4/F4rAzIzMxUenq6Nm7cKElyu91KTEzUwoULtWTJkgv65+XlyeVyafv27d62m2++WampqSotLe3wNX7/+98rIyNDX3zxha655pou1dXc3KyYmBg1NTUpOjraypJgwf76Js3c8L62L5xGmAH6GP5+oycE4v3b0tVMra2tqqmpUU5OzjcThIYqJydH1dXVHY6prq726S9Jubm5nfaXpKamJoWEhGjo0KFWygMAAP2QpY+ZTpw4ofb2dsXFxfm0x8XF6eDBgx2OcTqdHfZ3Op0d9v/yyy/1T//0T5ozZ85FE1pLS4taWlq8j5ubm7u6DAAA0If0qvvMtLW16e6775bH49Fzzz130b52u10xMTHeIzExsZuqBAAAvYmlMDNixAiFhYWpoaHBp72hoUHx8fEdjomPj+9S//NB5osvvtDOnTsv+blZcXGxmpqavMfRo0etLAUAAPQRlsJMeHi4Jk+erMrKSm+b2+1WZWWlsrKyOhyTlZXl01+Sdu7c6dP/fJD505/+pF27dmn48OGXrCUiIkLR0dE+BwAA6H8sX5pts9mUn5+vtLQ0ZWRkyOFwyOVyqaCgQJI0d+5cJSQkyG63S5IWLVqk7OxsrVu3TjNmzNCWLVu0d+9ebdq0SdLXQeZv/uZvVFtbq+3bt6u9vd17Ps2VV16p8HBuogYAADpnOczk5eXp+PHjWrFihZxOp1JTU1VRUeE9yffIkSMKDf1mw2fKlCkqKyvTsmXLtHTpUo0bN07btm1TcnKyJKm+vl6vvfaaJCk1NdXntd5++21Nnz7dz6UBAID+wK87ABcWFqqwsLDD56qqqi5omz17tmbPnt1h/6SkJFm81Q26IBjfnyTxHUoAgN6H72bqg4L5/UkS36EEAOhdCDN9UDC/P0niO5QAAL0LYaYP4/uTAAD9Qa+6aR4AAIBVhBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEbj0mwAgI9g3Omb+1MhmAgzAABJXweOqIFhKiqvC/jcUQPDtGtxNoEGQUGYAQBIkhKGRmnX4uyAf6/b4cYzKiqv00lXK2EGQUGYAQB4JQyNInDAOJwADAAAjEaYAQAARuNjJgCA0epPnQv4eT4SV2CZhDADADBW/alzyln3js61tQd8bq7AMgdhBgBgrJOuVp1ra5cjL1VjYwcHbF6uwDILYaaHBWN7NBg3vAKA3mxs7GAlJ8T0dBnoIYSZHhTs7dFhg8IDPi8A+CsY/9DiH2+QCDNdEqyTyw43ngnK9qjEiWsAeo9g3llYCu4/3oIVlvh/dGARZi4hmLsn0td/CdNHX8kPNYA+K1h3Fj4vGMGgOwIYJxcHDmHmEoJ1ctl5pHMA/YFpdxYOZgDj5OLAI8x0ESeXAUD/YloA68+4AzAAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKNxNRMAAD2AG/IFDmEGAIBuxA35As+vMFNSUqKnn35aTqdTKSkp2rBhgzIyMjrtv3XrVi1fvlyff/65xo0bpzVr1uj222/3Pu/xeLRy5Uo9//zzOnXqlKZOnarnnntO48aN86c8AAB6re64Id/vP/uLTvajr8mxHGbKy8tls9lUWlqqzMxMORwO5ebm6tChQ4qNjb2g/+7duzVnzhzZ7XbNnDlTZWVlmjVrlmpra5WcnCxJ+td//Vf97Gc/069+9SuNHj1ay5cvV25urg4cOKDIyMjLXyUAAL1IsG7IF8xdn9684xPi8Xg8VgZkZmYqPT1dGzdulCS53W4lJiZq4cKFWrJkyQX98/Ly5HK5tH37dm/bzTffrNTUVJWWlsrj8WjUqFFavHix/vEf/1GS1NTUpLi4OL300ku65557ulRXc3OzYmJi1NTUpOjoaCtLuqj99U2aueF9bV84jTsAAwB6vWB8OfL5HZ9gvBcG4v3b0s5Ma2urampqVFxc7G0LDQ1VTk6OqqurOxxTXV0tm83m05abm6tt27ZJkj777DM5nU7l5OR4n4+JiVFmZqaqq6s7DTMtLS1qaWnxPm5qapL09W9KIJ053Sx3y1mdOd2s5uaQgM4NAECgDQmVhgwJ7PvVmdPuoL0Xnn/ftri34sNSmDlx4oTa29sVFxfn0x4XF6eDBw92OMbpdHbY3+l0ep8/39ZZn47Y7XatWrXqgvbExMRLL8QPWY6gTAsAgDGC+V54+vRpxcT4t+tj7NVMxcXFPjs+brdbf/nLXzR8+HCFhJixg9Lc3KzExEQdPXo0oB+N9RZ9eX19eW0S6zMd6zNbf1ufx+PR6dOnNWrUKL/ntBRmRowYobCwMDU0NPi0NzQ0KD4+vsMx8fHxF+1//r8NDQ0aOXKkT5/U1NROa4mIiFBERIRP29ChQ7u6lF4lOjq6T/7AnteX19eX1yaxPtOxPrP1p/X5uyNznqU7AIeHh2vy5MmqrKz0trndblVWViorK6vDMVlZWT79JWnnzp3e/qNHj1Z8fLxPn+bmZn344YedzgkAAHCe5Y+ZbDab8vPzlZaWpoyMDDkcDrlcLhUUFEiS5s6dq4SEBNntdknSokWLlJ2drXXr1mnGjBnasmWL9u7dq02bNkmSQkJCVFRUpJ/+9KcaN26c99LsUaNGadasWYFbKQAA6JMsh5m8vDwdP35cK1askNPpVGpqqioqKrwn8B45ckShod9s+EyZMkVlZWVatmyZli5dqnHjxmnbtm3ee8xI0uOPPy6Xy6WHH35Yp06d0rRp01RRUdHn7zETERGhlStXXvBxWV/Rl9fXl9cmsT7TsT6zsT7rLN9nBgAAoDfhW7MBAIDRCDMAAMBohBkAAGA0wgwAADAaYSbISkpKlJSUpMjISGVmZmrPnj2d9p0+fbpCQkIuOGbMmNGNFXedlbVJksPh0PXXX6+oqCglJibq0Ucf1ZdfftlN1VpnZX1tbW168skndd111ykyMlIpKSmqqKjoxmqteffdd3XHHXdo1KhRCgkJ8X5X2sVUVVXppptuUkREhMaOHauXXnop6HX6y+r6/vznP+vee+/Vt771LYWGhqqoqKhb6vSX1fW98soruu2223TVVVcpOjpaWVlZeuONN7qnWD9YXd/777+vqVOnavjw4YqKitL48eP1zDPPdE+xfvDn7995v/vd7zRgwICL3lS2p1ldX1VVVYfvfRf7SqO/RpgJovLyctlsNq1cuVK1tbVKSUlRbm6uGhsbO+z/yiuv6M9//rP32L9/v8LCwjR79uxurvzSrK6trKxMS5Ys0cqVK/Xxxx/rhRdeUHl5uZYuXdrNlXeN1fUtW7ZMv/jFL7RhwwYdOHBA8+fP15133ql9+/Z1c+Vd43K5lJKSopKSki71/+yzzzRjxgzdeuutqqurU1FRkR566KFe+4ZodX0tLS266qqrtGzZMqWkpAS5ustndX3vvvuubrvtNu3YsUM1NTW69dZbdccdd/SZn89BgwapsLBQ7777rj7++GMtW7ZMy5Yt897PrLexur7zTp06pblz5+q73/1ukCoLDH/Xd+jQIZ/3wNjY2K4P9iBoMjIyPAsWLPA+bm9v94waNcpjt9u7NP6ZZ57xDBkyxHPmzJlgleg3q2tbsGCB5zvf+Y5Pm81m80ydOjWodfrL6vpGjhzp2bhxo0/bj370I899990X1DoDQZLn1VdfvWifxx9/3HPjjTf6tOXl5Xlyc3ODWFlgdGV9/1d2drZn0aJFQasn0Kyu77wJEyZ4Vq1aFfiCAszf9d15552e+++/P/AFBZiV9eXl5XmWLVvmWblypSclJSWodQVKV9b39ttveyR5Tp486ffrsDMTJK2traqpqVFOTo63LTQ0VDk5Oaquru7SHC+88ILuueceDRo0KFhl+sWftU2ZMkU1NTXej2o+/fRT7dixQ7fffnu31GyFP+traWm54CaPUVFRev/994Naa3eprq72+f2QpNzc3C7/LKN3cbvdOn36tK688sqeLiUo9u3bp927dys7O7unSwmYF198UZ9++qlWrlzZ06UETWpqqkaOHKnbbrtNv/vd7yyNNfZbs3u7EydOqL293Xtn5PPi4uJ08ODBS47fs2eP9u/frxdeeCFYJfrNn7Xde++9OnHihKZNmyaPx6OvvvpK8+fP75UfM/mzvtzcXK1fv1633HKLrrvuOlVWVuqVV15Re3t7d5QcdE6ns8Pfj+bmZp07d05RUVE9VBn8sXbtWp05c0Z33313T5cSUFdffbWOHz+ur776Sk888YQeeuihni4pIP70pz9pyZIleu+99zRgQN972x45cqRKS0uVlpamlpYW/fKXv9T06dP14Ycf6qabburSHOzM9FIvvPCCJk6cqIyMjJ4uJSCqqqr01FNP6ec//7lqa2v1yiuv6PXXX9dPfvKTni4tIJ599lmNGzdO48ePV3h4uAoLC1VQUODz1R5Ab1BWVqZVq1bpN7/5jbVzEgzw3nvvae/evSotLZXD4dDLL7/c0yVdtvb2dt17771atWqVvvWtb/V0OUFx/fXX6+/+7u80efJkTZkyRZs3b9aUKVMsncTd9yJeLzFixAiFhYWpoaHBp72hoUHx8fEXHetyubRlyxY9+eSTwSzRb/6sbfny5XrggQe8/1KaOHGi9/u4/vmf/7lXven7s76rrrpK27Zt05dffqn//d//1ahRo7RkyRKNGTOmO0oOuvj4+A5/P6Kjo9mVMciWLVv00EMPaevWrRd8bNgXjB49WtLX/39paGjQE088oTlz5vRwVZfn9OnT2rt3r/bt26fCwkJJX39M6PF4NGDAAL355pv6zne+08NVBl5GRoalj+l7zztIHxMeHq7JkyersrLS2+Z2u1VZWamsrKyLjt26dataWlp0//33B7tMv/iztrNnz14QWMLCwiRJnl729WCX82cXGRmphIQEffXVV/rtb3+rH/7wh8Eut1tkZWX5/H5I0s6dOy/5+4He4+WXX1ZBQYFefvnlXnu7h0Byu91qaWnp6TIuW3R0tP74xz+qrq7Oe8yfP1/XX3+96urqlJmZ2dMlBkVdXZ1GjhzZ5f7szASRzWZTfn6+0tLSlJGRIYfDIZfLpYKCAknS3LlzlZCQILvd7jPuhRde0KxZszR8+PCeKLtLrK7tjjvu0Pr16zVp0iRlZmbq8OHDWr58ue644w5vqOlNrK7vww8/VH19vVJTU1VfX68nnnhCbrdbjz/+eE8uo1NnzpzR4cOHvY8/++wz1dXV6corr9Q111yj4uJi1dfX69e//rUkaf78+dq4caMef/xxPfjgg3rrrbf0m9/8Rq+//npPLeGirK5P+vp/nufHHj9+XHV1dQoPD9eECRO6u/xLsrq+srIy5efn69lnn1VmZqb3/h1RUVGKiYnpkTVcjNX1lZSU6JprrtH48eMlfX0p+tq1a/XII4/0SP2XYmV9oaGhSk5O9hkfGxuryMjIC9p7C6t/fg6HQ6NHj9aNN96oL7/8Ur/85S/11ltv6c033+z6i/p9HRS6ZMOGDZ5rrrnGEx4e7snIyPB88MEH3ueys7M9+fn5Pv0PHjzokeR58803u7lS66ysra2tzfPEE094rrvuOk9kZKQnMTHR8w//8A+XdSlesFlZX1VVleeGG27wREREeIYPH+554IEHPPX19T1QddecvxTyr4/za8rPz/dkZ2dfMCY1NdUTHh7uGTNmjOfFF1/s9rq7yp/1ddT/2muv7fbau8Lq+rKzsy/av7exur6f/exnnhtvvNFzxRVXeKKjoz2TJk3y/PznP/e0t7f3zAIuwZ+fz/+rt1+abXV9a9as8b43XHnllZ7p06d73nrrLUuvGeLx9LI9fgAAAAs4ZwYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAo/0/L/MjwiGkigIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 10 time is now 0 sec\n",
      "working on HD 607 time is now 2 sec\n",
      "working on HD 1176 time is now 5 sec\n",
      "working on HD 1696 time is now 7 sec\n",
      "working on HD 2238 time is now 10 sec\n",
      "working on HD 2788 time is now 14 sec\n",
      "working on HD 3365 time is now 17 sec\n",
      "working on HD 3877 time is now 20 sec\n",
      "working on HD 4567 time is now 23 sec\n",
      "out of 4207 total HDs, there were 4207 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 25 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "f4bb76c7-0ae0-40cb-9c6a-ee436dfc30c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDunitList = [savedAgainUnitList[t].copy() for t in range(nHDs)]  #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "0.81163     0     4378 0.5\n",
      "0.90386     1     2196 0.5\n",
      "0.96497     4143     154316 0.25\n",
      "0.84962     4144     24060 0.25\n",
      "0.95522     4145     88252 0.25\n",
      "0.85902     4146     125341 0.25\n",
      "0.90369     4147     160383 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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6AADAbJbCTHh4uCZPnqzKykpvm9vtVmVlpbKysvwqwN85IyIiFB0d7XMAAID+x/Kl2TabTfn5+UpLS1NGRoYcDodcLpcKCgokSXPnzlVCQoLsdrukr0/wPXDggPfX9fX1qqur0+DBgzV27NguzQkAANAZy2EmLy9Px48f14oVK+R0OpWamqqKigrvCbxHjhxRaOg3Gz7Hjh3TpEmTvI/Xrl2rtWvXKjs7W1VVVV2aEwAAoDN+3QG4sLBQhYWFHT53PqCcl5SUJI/Hc1lzAgAAdMbYq5kAAAAkwgwAADAcYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABhtQE8XAJis/tQ5nXS1Bnzew41nAj4nAPRVhBnAT/Wnziln3Ts619YelPmjBoZp2KDwoMwNAH0JYQbw00lXq861tcuRl6qxsYMDPv+wQeFKGBoV8HkBoK8hzACXaWzsYCUnxPR0GQDQb/l1AnBJSYmSkpIUGRmpzMxM7dmz56L9t27dqvHjxysyMlITJ07Ujh07fJ4/c+aMCgsLdfXVVysqKkoTJkxQaWmpP6UBAIB+xnKYKS8vl81m08qVK1VbW6uUlBTl5uaqsbGxw/67d+/WnDlz9OMf/1j79u3TrFmzNGvWLO3fv9/bx2azqaKiQv/+7/+ujz/+WEVFRSosLNRrr73m/8oAAEC/YDnMrF+/XvPmzVNBQYF3B+WKK67Q5s2bO+z/7LPP6vvf/74ee+wx3XDDDfrJT36im266SRs3bvT22b17t/Lz8zV9+nQlJSXp4YcfVkpKyiV3fAAAACyFmdbWVtXU1CgnJ+ebCUJDlZOTo+rq6g7HVFdX+/SXpNzcXJ/+U6ZM0Wuvvab6+np5PB69/fbb+uSTT/S9732v01paWlrU3NzscwAAgP7HUpg5ceKE2tvbFRcX59MeFxcnp9PZ4Rin03nJ/hs2bNCECRN09dVXKzw8XN///vdVUlKiW265pdNa7Ha7YmJivEdiYqKVpQAAgD6iV9wBeMOGDfrggw/02muvqaamRuvWrdOCBQu0a9euTscUFxerqanJexw9erQbKwYAAL2FpUuzR4wYobCwMDU0NPi0NzQ0KD4+vsMx8fHxF+1/7tw5LV26VK+++qpmzJghSfr2t7+turo6rV279oKPqM6LiIhQRESElfIBAEAfZGlnJjw8XJMnT1ZlZaW3ze12q7KyUllZWR2OycrK8ukvSTt37vT2b2trU1tbm0JDfUsJCwuT2+22Uh4AAOiHLN80z2azKT8/X2lpacrIyJDD4ZDL5VJBQYEkae7cuUpISJDdbpckLVq0SNnZ2Vq3bp1mzJihLVu2aO/evdq0aZMkKTo6WtnZ2XrssccUFRWla6+9Vu+8845+/etfa/369QFcKgAA6Issh5m8vDwdP35cK1askNPpVGpqqioqKrwn+R45csRnl2XKlCkqKyvTsmXLtHTpUo0bN07btm1TcnKyt8+WLVtUXFys++67T3/5y1907bXX6l/+5V80f/78ACwRAAD0ZSEej8fT00UEQnNzs2JiYtTU1KTo6OieLgf9wP76Js3c8L62L5zG1xkYij9DoOcF4v27V1zNBAAA4C/CDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0fwKMyUlJUpKSlJkZKQyMzO1Z8+ei/bfunWrxo8fr8jISE2cOFE7duy4oM/HH3+sH/zgB4qJidGgQYOUnp6uI0eO+FMeAADoRyyHmfLyctlsNq1cuVK1tbVKSUlRbm6uGhsbO+y/e/duzZkzRz/+8Y+1b98+zZo1S7NmzdL+/fu9ff7nf/5H06ZN0/jx41VVVaU//OEPWr58uSIjI/1fGQAA6BdCPB6Px8qAzMxMpaena+PGjZIkt9utxMRELVy4UEuWLLmgf15enlwul7Zv3+5tu/nmm5WamqrS0lJJ0j333KOBAwfq3/7t3/xeSHNzs2JiYtTU1KTo6Gi/50HPqT91TiddrUGZe9igcCUMjQronPvrmzRzw/vavnCakhNiAjo3usf5P0NHXqrGxg4O+PzB+LkD+ppAvH8PsNK5tbVVNTU1Ki4u9raFhoYqJydH1dXVHY6prq6WzWbzacvNzdW2bdskfR2GXn/9dT3++OPKzc3Vvn37NHr0aBUXF2vWrFmd1tLS0qKWlhbv4+bmZitLQS9Tf+qccta9o3Nt7UGZP2pgmHYtzuaNBT6GDQpX1MAwFZXXBWV+fu6A7mEpzJw4cULt7e2Ki4vzaY+Li9PBgwc7HON0Ojvs73Q6JUmNjY06c+aMVq9erZ/+9Kdas2aNKioq9KMf/Uhvv/22srOzO5zXbrdr1apVVspHL3bS1apzbe1B+Rfy4cYzKiqv00lXK28q8JEwNEq7FmcHZUeQnzug+1gKM8HgdrslST/84Q/16KOPSpJSU1O1e/dulZaWdhpmiouLfXZ8mpublZiYGPyCEVRjYwfzkQ26VcLQKMIGYDhLYWbEiBEKCwtTQ0ODT3tDQ4Pi4+M7HBMfH3/R/iNGjNCAAQM0YcIEnz433HCD3n///U5riYiIUEREhJXyAQBAH2Tpaqbw8HBNnjxZlZWV3ja3263KykplZWV1OCYrK8unvyTt3LnT2z88PFzp6ek6dOiQT59PPvlE1157rZXyAABAP2T5Yyabzab8/HylpaUpIyNDDodDLpdLBQUFkqS5c+cqISFBdrtdkrRo0SJlZ2dr3bp1mjFjhrZs2aK9e/dq06ZN3jkfe+wx5eXl6ZZbbtGtt96qiooK/dd//ZeqqqoCs0oAANBnWQ4zeXl5On78uFasWCGn06nU1FRVVFR4T/I9cuSIQkO/2fCZMmWKysrKtGzZMi1dulTjxo3Ttm3blJyc7O1z5513qrS0VHa7XY888oiuv/56/fa3v9W0adMCsEQAANCX+XUCcGFhoQoLCzt8rqPdlNmzZ2v27NkXnfPBBx/Ugw8+6E85AACgH+O7mQAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMNqAni4A6C6HG8/06vkAAP4hzKDPGzYoXFEDw1RUXhfwuaMGhmnYoPCAzwsA6DrCDPq8hKFR2rU4WyddrQGfe9igcCUMjQr4vACArvPrnJmSkhIlJSUpMjJSmZmZ2rNnz0X7b926VePHj1dkZKQmTpyoHTt2dNp3/vz5CgkJkcPh8Kc0oEMJQ6OUnBAT8IMgAwA9z/LOTHl5uWw2m0pLS5WZmSmHw6Hc3FwdOnRIsbGxF/TfvXu35syZI7vdrpkzZ6qsrEyzZs1SbW2tkpOTffq++uqr+uCDDzRq1Cj/VwQAvUgwzq1iRxDwFeLxeDxWBmRmZio9PV0bN26UJLndbiUmJmrhwoVasmTJBf3z8vLkcrm0fft2b9vNN9+s1NRUlZaWetvq6+uVmZmpN954QzNmzFBRUZGKioq6XFdzc7NiYmLU1NSk6OhoK0tCL7C/vkkzN7yv7QunKTkhpqfLAS5b/alzyln3js61tQd87qiBYdq1OJtAgz4hEO/flnZmWltbVVNTo+LiYm9baGiocnJyVF1d3eGY6upq2Ww2n7bc3Fxt27bN+9jtduuBBx7QY489phtvvLFLtbS0tKilpcX7uLm52cJKACC4gnWu1uHGMyoqr9NJVythBvj/LIWZEydOqL29XXFxcT7tcXFxOnjwYIdjnE5nh/2dTqf38Zo1azRgwAA98sgjXa7Fbrdr1apVFqoHgO6VMDSKwAF0gx6/aV5NTY2effZZvfTSSwoJCenyuOLiYjU1NXmPo0ePBrFKAADQW1kKMyNGjFBYWJgaGhp82hsaGhQfH9/hmPj4+Iv2f++999TY2KhrrrlGAwYM0IABA/TFF19o8eLFSkpK6rSWiIgIRUdH+xwAAKD/sRRmwsPDNXnyZFVWVnrb3G63KisrlZWV1eGYrKwsn/6StHPnTm//Bx54QH/4wx9UV1fnPUaNGqXHHntMb7zxhtX1AACAfsbypdk2m035+flKS0tTRkaGHA6HXC6XCgoKJElz585VQkKC7Ha7JGnRokXKzs7WunXrNGPGDG3ZskV79+7Vpk2bJEnDhw/X8OHDfV5j4MCBio+P1/XXX3+56wMAAH2c5TCTl5en48ePa8WKFXI6nUpNTVVFRYX3JN8jR44oNPSbDZ8pU6aorKxMy5Yt09KlSzVu3Dht27btgnvMAAAA+MOvrzMoLCxUYWFhh89VVVVd0DZ79mzNnj27y/N//vnn/pQFAAD6oR6/mgkAAOByEGYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMNqCnCwAAWHe48UxQ5h02KFwJQ6OCMjcQLIQZADDIsEHhihoYpqLyuqDMHzUwTLsWZxNoYBTCDAAYJGFolHYtztZJV2vA5z7ceEZF5XU66WolzMAohBkAMEzC0CjCBvB/cAIwAAAwGjszsKz+1LmAb3EH62RGAEDfR5iBJfWnziln3Ts619Ye8LmjBoZp2KDwgM8LAOjb/AozJSUlevrpp+V0OpWSkqINGzYoIyOj0/5bt27V8uXL9fnnn2vcuHFas2aNbr/9dklSW1ubli1bph07dujTTz9VTEyMcnJytHr1ao0aNcq/VSFoTrpada6tXY68VI2NHRzQubkkFADgD8thpry8XDabTaWlpcrMzJTD4VBubq4OHTqk2NjYC/rv3r1bc+bMkd1u18yZM1VWVqZZs2aptrZWycnJOnv2rGpra7V8+XKlpKTo5MmTWrRokX7wgx9o7969AVkkAm9s7GAlJ8T0dBkAACjE4/F4rAzIzMxUenq6Nm7cKElyu91KTEzUwoULtWTJkgv65+XlyeVyafv27d62m2++WampqSotLe3wNX7/+98rIyNDX3zxha655pou1dXc3KyYmBg1NTUpOjraypJgwf76Js3c8L62L5xGmAH6GP5+oycE4v3b0tVMra2tqqmpUU5OzjcThIYqJydH1dXVHY6prq726S9Jubm5nfaXpKamJoWEhGjo0KFWygMAAP2QpY+ZTpw4ofb2dsXFxfm0x8XF6eDBgx2OcTqdHfZ3Op0d9v/yyy/1T//0T5ozZ85FE1pLS4taWlq8j5ubm7u6DAAA0If0qvvMtLW16e6775bH49Fzzz130b52u10xMTHeIzExsZuqBAAAvYmlMDNixAiFhYWpoaHBp72hoUHx8fEdjomPj+9S//NB5osvvtDOnTsv+blZcXGxmpqavMfRo0etLAUAAPQRlsJMeHi4Jk+erMrKSm+b2+1WZWWlsrKyOhyTlZXl01+Sdu7c6dP/fJD505/+pF27dmn48OGXrCUiIkLR0dE+BwAA6H8sX5pts9mUn5+vtLQ0ZWRkyOFwyOVyqaCgQJI0d+5cJSQkyG63S5IWLVqk7OxsrVu3TjNmzNCWLVu0d+9ebdq0SdLXQeZv/uZvVFtbq+3bt6u9vd17Ps2VV16p8HBuogYAADpnOczk5eXp+PHjWrFihZxOp1JTU1VRUeE9yffIkSMKDf1mw2fKlCkqKyvTsmXLtHTpUo0bN07btm1TcnKyJKm+vl6vvfaaJCk1NdXntd5++21Nnz7dz6UBAID+wK87ABcWFqqwsLDD56qqqi5omz17tmbPnt1h/6SkJFm81Q26IBjfnyTxHUoAgN6H72bqg4L5/UkS36EEAOhdCDN9UDC/P0niO5QAAL0LYaYP4/uTAAD9Qa+6aR4AAIBVhBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEbj0mwAgI9g3Omb+1MhmAgzAABJXweOqIFhKiqvC/jcUQPDtGtxNoEGQUGYAQBIkhKGRmnX4uyAf6/b4cYzKiqv00lXK2EGQUGYAQB4JQyNInDAOJwADAAAjEaYAQAARuNjJgCA0epPnQv4eT4SV2CZhDADADBW/alzyln3js61tQd8bq7AMgdhBgBgrJOuVp1ra5cjL1VjYwcHbF6uwDILYaaHBWN7NBg3vAKA3mxs7GAlJ8T0dBnoIYSZHhTs7dFhg8IDPi8A+CsY/9DiH2+QCDNdEqyTyw43ngnK9qjEiWsAeo9g3llYCu4/3oIVlvh/dGARZi4hmLsn0td/CdNHX8kPNYA+K1h3Fj4vGMGgOwIYJxcHDmHmEoJ1ctl5pHMA/YFpdxYOZgDj5OLAI8x0ESeXAUD/YloA68+4AzAAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKNxNRMAAD2AG/IFDmEGAIBuxA35As+vMFNSUqKnn35aTqdTKSkp2rBhgzIyMjrtv3XrVi1fvlyff/65xo0bpzVr1uj222/3Pu/xeLRy5Uo9//zzOnXqlKZOnarnnntO48aN86c8AAB6re64Id/vP/uLTvajr8mxHGbKy8tls9lUWlqqzMxMORwO5ebm6tChQ4qNjb2g/+7duzVnzhzZ7XbNnDlTZWVlmjVrlmpra5WcnCxJ+td//Vf97Gc/069+9SuNHj1ay5cvV25urg4cOKDIyMjLXyUAAL1IsG7IF8xdn9684xPi8Xg8VgZkZmYqPT1dGzdulCS53W4lJiZq4cKFWrJkyQX98/Ly5HK5tH37dm/bzTffrNTUVJWWlsrj8WjUqFFavHix/vEf/1GS1NTUpLi4OL300ku65557ulRXc3OzYmJi1NTUpOjoaCtLuqj99U2aueF9bV84jTsAAwB6vWB8OfL5HZ9gvBcG4v3b0s5Ma2urampqVFxc7G0LDQ1VTk6OqqurOxxTXV0tm83m05abm6tt27ZJkj777DM5nU7l5OR4n4+JiVFmZqaqq6s7DTMtLS1qaWnxPm5qapL09W9KIJ053Sx3y1mdOd2s5uaQgM4NAECgDQmVhgwJ7PvVmdPuoL0Xnn/ftri34sNSmDlx4oTa29sVFxfn0x4XF6eDBw92OMbpdHbY3+l0ep8/39ZZn47Y7XatWrXqgvbExMRLL8QPWY6gTAsAgDGC+V54+vRpxcT4t+tj7NVMxcXFPjs+brdbf/nLXzR8+HCFhJixg9Lc3KzExEQdPXo0oB+N9RZ9eX19eW0S6zMd6zNbf1ufx+PR6dOnNWrUKL/ntBRmRowYobCwMDU0NPi0NzQ0KD4+vsMx8fHxF+1//r8NDQ0aOXKkT5/U1NROa4mIiFBERIRP29ChQ7u6lF4lOjq6T/7AnteX19eX1yaxPtOxPrP1p/X5uyNznqU7AIeHh2vy5MmqrKz0trndblVWViorK6vDMVlZWT79JWnnzp3e/qNHj1Z8fLxPn+bmZn344YedzgkAAHCe5Y+ZbDab8vPzlZaWpoyMDDkcDrlcLhUUFEiS5s6dq4SEBNntdknSokWLlJ2drXXr1mnGjBnasmWL9u7dq02bNkmSQkJCVFRUpJ/+9KcaN26c99LsUaNGadasWYFbKQAA6JMsh5m8vDwdP35cK1askNPpVGpqqioqKrwn8B45ckShod9s+EyZMkVlZWVatmyZli5dqnHjxmnbtm3ee8xI0uOPPy6Xy6WHH35Yp06d0rRp01RRUdHn7zETERGhlStXXvBxWV/Rl9fXl9cmsT7TsT6zsT7rLN9nBgAAoDfhW7MBAIDRCDMAAMBohBkAAGA0wgwAADAaYSbISkpKlJSUpMjISGVmZmrPnj2d9p0+fbpCQkIuOGbMmNGNFXedlbVJksPh0PXXX6+oqCglJibq0Ucf1ZdfftlN1VpnZX1tbW168skndd111ykyMlIpKSmqqKjoxmqteffdd3XHHXdo1KhRCgkJ8X5X2sVUVVXppptuUkREhMaOHauXXnop6HX6y+r6/vznP+vee+/Vt771LYWGhqqoqKhb6vSX1fW98soruu2223TVVVcpOjpaWVlZeuONN7qnWD9YXd/777+vqVOnavjw4YqKitL48eP1zDPPdE+xfvDn7995v/vd7zRgwICL3lS2p1ldX1VVVYfvfRf7SqO/RpgJovLyctlsNq1cuVK1tbVKSUlRbm6uGhsbO+z/yiuv6M9//rP32L9/v8LCwjR79uxurvzSrK6trKxMS5Ys0cqVK/Xxxx/rhRdeUHl5uZYuXdrNlXeN1fUtW7ZMv/jFL7RhwwYdOHBA8+fP15133ql9+/Z1c+Vd43K5lJKSopKSki71/+yzzzRjxgzdeuutqqurU1FRkR566KFe+4ZodX0tLS266qqrtGzZMqWkpAS5ustndX3vvvuubrvtNu3YsUM1NTW69dZbdccdd/SZn89BgwapsLBQ7777rj7++GMtW7ZMy5Yt897PrLexur7zTp06pblz5+q73/1ukCoLDH/Xd+jQIZ/3wNjY2K4P9iBoMjIyPAsWLPA+bm9v94waNcpjt9u7NP6ZZ57xDBkyxHPmzJlgleg3q2tbsGCB5zvf+Y5Pm81m80ydOjWodfrL6vpGjhzp2bhxo0/bj370I899990X1DoDQZLn1VdfvWifxx9/3HPjjTf6tOXl5Xlyc3ODWFlgdGV9/1d2drZn0aJFQasn0Kyu77wJEyZ4Vq1aFfiCAszf9d15552e+++/P/AFBZiV9eXl5XmWLVvmWblypSclJSWodQVKV9b39ttveyR5Tp486ffrsDMTJK2traqpqVFOTo63LTQ0VDk5Oaquru7SHC+88ILuueceDRo0KFhl+sWftU2ZMkU1NTXej2o+/fRT7dixQ7fffnu31GyFP+traWm54CaPUVFRev/994Naa3eprq72+f2QpNzc3C7/LKN3cbvdOn36tK688sqeLiUo9u3bp927dys7O7unSwmYF198UZ9++qlWrlzZ06UETWpqqkaOHKnbbrtNv/vd7yyNNfZbs3u7EydOqL293Xtn5PPi4uJ08ODBS47fs2eP9u/frxdeeCFYJfrNn7Xde++9OnHihKZNmyaPx6OvvvpK8+fP75UfM/mzvtzcXK1fv1633HKLrrvuOlVWVuqVV15Re3t7d5QcdE6ns8Pfj+bmZp07d05RUVE9VBn8sXbtWp05c0Z33313T5cSUFdffbWOHz+ur776Sk888YQeeuihni4pIP70pz9pyZIleu+99zRgQN972x45cqRKS0uVlpamlpYW/fKXv9T06dP14Ycf6qabburSHOzM9FIvvPCCJk6cqIyMjJ4uJSCqqqr01FNP6ec//7lqa2v1yiuv6PXXX9dPfvKTni4tIJ599lmNGzdO48ePV3h4uAoLC1VQUODz1R5Ab1BWVqZVq1bpN7/5jbVzEgzw3nvvae/evSotLZXD4dDLL7/c0yVdtvb2dt17771atWqVvvWtb/V0OUFx/fXX6+/+7u80efJkTZkyRZs3b9aUKVMsncTd9yJeLzFixAiFhYWpoaHBp72hoUHx8fEXHetyubRlyxY9+eSTwSzRb/6sbfny5XrggQe8/1KaOHGi9/u4/vmf/7lXven7s76rrrpK27Zt05dffqn//d//1ahRo7RkyRKNGTOmO0oOuvj4+A5/P6Kjo9mVMciWLVv00EMPaevWrRd8bNgXjB49WtLX/39paGjQE088oTlz5vRwVZfn9OnT2rt3r/bt26fCwkJJX39M6PF4NGDAAL355pv6zne+08NVBl5GRoalj+l7zztIHxMeHq7JkyersrLS2+Z2u1VZWamsrKyLjt26dataWlp0//33B7tMv/iztrNnz14QWMLCwiRJnl729WCX82cXGRmphIQEffXVV/rtb3+rH/7wh8Eut1tkZWX5/H5I0s6dOy/5+4He4+WXX1ZBQYFefvnlXnu7h0Byu91qaWnp6TIuW3R0tP74xz+qrq7Oe8yfP1/XX3+96urqlJmZ2dMlBkVdXZ1GjhzZ5f7szASRzWZTfn6+0tLSlJGRIYfDIZfLpYKCAknS3LlzlZCQILvd7jPuhRde0KxZszR8+PCeKLtLrK7tjjvu0Pr16zVp0iRlZmbq8OHDWr58ue644w5vqOlNrK7vww8/VH19vVJTU1VfX68nnnhCbrdbjz/+eE8uo1NnzpzR4cOHvY8/++wz1dXV6corr9Q111yj4uJi1dfX69e//rUkaf78+dq4caMef/xxPfjgg3rrrbf0m9/8Rq+//npPLeGirK5P+vp/nufHHj9+XHV1dQoPD9eECRO6u/xLsrq+srIy5efn69lnn1VmZqb3/h1RUVGKiYnpkTVcjNX1lZSU6JprrtH48eMlfX0p+tq1a/XII4/0SP2XYmV9oaGhSk5O9hkfGxuryMjIC9p7C6t/fg6HQ6NHj9aNN96oL7/8Ur/85S/11ltv6c033+z6i/p9HRS6ZMOGDZ5rrrnGEx4e7snIyPB88MEH3ueys7M9+fn5Pv0PHjzokeR58803u7lS66ysra2tzfPEE094rrvuOk9kZKQnMTHR8w//8A+XdSlesFlZX1VVleeGG27wREREeIYPH+554IEHPPX19T1QddecvxTyr4/za8rPz/dkZ2dfMCY1NdUTHh7uGTNmjOfFF1/s9rq7yp/1ddT/2muv7fbau8Lq+rKzsy/av7exur6f/exnnhtvvNFzxRVXeKKjoz2TJk3y/PznP/e0t7f3zAIuwZ+fz/+rt1+abXV9a9as8b43XHnllZ7p06d73nrrLUuvGeLx9LI9fgAAAAs4ZwYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAo/0/L/MjwiGkigIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00769 0.11834\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c5d76a90-4441-4e96-b9a7-7de433686812",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving pre-squaring HD vtd lists to file\n"
     ]
    }
   ],
   "source": [
    "print(\"OPTIONAL - saving pre-squaring HD vtd lists to file.  SKIP FOR vtd-frag states; see a later block\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"contigNotSquaredVTD.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 10\n",
      "working on avg unit-to-HDcp dist for HD 976\n",
      "working on avg unit-to-HDcp dist for HD 1837\n",
      "working on avg unit-to-HDcp dist for HD 2654\n",
      "working on avg unit-to-HDcp dist for HD 3565\n",
      "working on avg unit-to-HDcp dist for HD 4567\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "f179943a-ef19-4036-a8c6-5ab7b1f97bf8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Restart\n",
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "512b2e80-04a3-4bd5-9537-ba6a2b9d81cb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4144, 4146, 4147]\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = [4144, 4146, 4147]\n",
    "print(list(barredJettisonSet))\n",
    "MIpopRat = [1.022, 1.08, 1.09] #recording the pop / aDP maximum for allowing tack-on of corner clusters for each of these underused corner clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "9854465d-3163-4fa2-a151-7862a1396df3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "how much HD weight could pick up our CCB as a neighbor?\n",
      "we will boost unitUse of 4147 from 0.90369 to 0.92369 by attaching to all neighboring HDs with after-added pop no more than 1.09 aDP\n"
     ]
    }
   ],
   "source": [
    "print(\"how much HD weight could pick up our CCB as a neighbor?\")  #run this for each of of the underused corner clusters.  iterate on popRat\n",
    "boostWeight, CCBu = 0., list(barredJettisonSet)[2]  #0 #1 #2\n",
    "CCBnbrSet = set(unitNbrs[CCBu]) #set(get2nbrs([CCBu], unitNbrs))\n",
    "receivingList = list()\n",
    "popRat = 1.09\n",
    "for t in popHDlist:\n",
    "    if HDvPop[t] < popRat*aDP - unitPop[CCBu] and CCBu not in HDunitList[t]:  #1.336\n",
    "        if len(CCBnbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            boostWeight += HDweight[t] * nDistricts\n",
    "            receivingList.append(t)\n",
    "print(\"we will boost unitUse of\",CCBu,\"from\",r5(unitUse[CCBu]),\"to\",r5(unitUse[CCBu]+boostWeight),\"by attaching to all neighboring HDs\",\n",
    "     \"with after-added pop no more than\",popRat,\"aDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "2f89cffc-2050-429c-93da-192fac45bd7d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, for CCB unit 4147 tack on this CCBu where it won't overshoot pop too much and won't create enclaves or district discontiguity\n",
      "After CCBu tack-on, its use is now 0.92369\n"
     ]
    }
   ],
   "source": [
    "print(\"now, for CCB unit\",CCBu,\"tack on this CCBu where it won't overshoot pop too much and won't create enclaves or district discontiguity\")\n",
    "for t in receivingList: \n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t]+[CCBu],unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        HDunitList[t].append(CCBu)\n",
    "        HDvPop[t] += unitPop[CCBu]\n",
    "        unitUse[CCBu] += HDweight[t] * nDistricts\n",
    "print(\"After CCBu tack-on, its use is now\",r5(unitUse[CCBu]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "7f72a570-aa0c-480e-b049-ccebbe24e51f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 10 time is now 0 sec\n",
      "working on HD 607 time is now 2 sec\n",
      "working on HD 1176 time is now 5 sec\n",
      "working on HD 1696 time is now 7 sec\n",
      "working on HD 2238 time is now 10 sec\n",
      "working on HD 2788 time is now 12 sec\n",
      "working on HD 3365 time is now 15 sec\n",
      "working on HD 3877 time is now 18 sec\n",
      "working on HD 4567 time is now 20 sec\n",
      "out of 4207 total HDs, there were 4207 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 21 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "f5b99900-614a-47df-b7de-5d59fffd6d3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00769 0.11834\n"
     ]
    }
   ],
   "source": [
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()\n",
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "fa933848-9596-4201-8e7b-5dfbf4453ea8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"ContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 899 HDs with pop < 767121 to within 1908\n",
      "working on squaring up HD 31 time is now 0\n",
      "working on squaring up HD 877 time is now 62\n",
      "working on squaring up HD 1733 time is now 108\n",
      "working on squaring up HD 2547 time is now 196\n",
      "working on squaring up HD 2760 time is now 211\n",
      "working on squaring up HD 3225 time is now 250\n",
      "working on squaring up HD 3564 time is now 266\n",
      "working on squaring up HD 3862 time is now 330\n",
      "working on squaring up HD 4558 time is now 386\n",
      "We have addressed underpop in a total of 899 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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+dGo//m7Omq/w53uHe2RgCYMQUQf5w3IC1D6GXe9lbs6as6b9pSuuJXD1d/P5Tw/DZAJ+u+kg6i/9PEjnjW1AWGgQXrxnCG4eEOF0EDp3sRmJywvBtpf2XWwyWQRTd3J6Zmkiuqq9fgXWVp4m32OrE60Cnp8LRc4mDo7Gmw+MgKO/YuZ+PE+s3W8RgszqLzVjzpr92HPsLMJCgpw+P4Yg+wh4pq8dgxCRC/jqcgJkH4bdq4wmgdLvz+Lj8h/a7HDsKb8aGoM3piZ1yr5fKalCfUProESu54mBJWwaI3IRdy8nQO5lDrvXdqLVymTSTGsLkWrVKjx3141ec+2/GhqNGcfjkfvfRUjlJlQZiEtNjs3q7I3c3deOQYjIhdy1nAB5hlzDbmFFndU+ODpDI+as2Y+33FDrae+M3uMSomQbhDIGa1F/qQkl3/p2h2x397VjECIicoDcwq7RJPD0RwfbLfP0Rwdxe6K20wKhQ8vXeE9rndv9080zS3eGAAWQ3Cfcvcd069GIiMin7P7+rNWOxC3VX2rG7u/PdsrxHZ3R+8zFRnhCtCYYT6XdgLlj+3vk+P7CJICy4+fdekwGIQ/w5g6HREQtXZ1Xx3XlHOHMjN6emMKgR9cgPJMxCCP79oDh8hW3H9/fsI+Qn+MK5UTkW+xt7nJ9s5gjy9ek9u8Jo0nAZBIICwly6yivcxebMdeO2eXJPu4Os6wRciMu2khEvsbe/lCd0W/KkRm9CyvqcMsft+LB3D0uDUH+3Q3eu3hqPi4GITfhoo1E5ItG9euJsND2JxMMDw3CqH6uD0L21gzUnLlo9Y9MV+gWzIYTdxHwzHxcDEJuwhXKicgXBQYo8OI9Q9ots+KeIZ3y5WXvjN7r9tZ22mCxC+zz41Zlx93/Hcgg5CZctJGIfNXEwdF466EkaNWWNTTRmuBOnUPInhm97/9FnMUkj+Tb3vmiBisKDrv1mKzzcxMu2khEHWXvpIKdwVOTSbY1o3eUWoX7fxGHQyfrO/X45H7vfFGNhRMGQdnFPXU1DEJuwhXKiagjvGHEqbsmk7w28N2eqLUIYTVnLiFvVzVeKanq9HPxFAXkOzekSQAflNZgxq393HI8BiE3MVfxPr5mf6sfcDkt2khEjjOPOL32i9E84tTXFvZtr2bLVuArOFCHlcXfOXS88NAgvHD3EPzus8PQGXyj+4FCIe9V64+fu+S2YzEIuZHcF20kIsfZGnGqwNURp525xIUrtRd0ALQb+GbeGo/cnTUOH1MASB+sRfrgq7VKxYd1LlmPTNklAE1XTB3ejzVyH0Dcp0eo246lEELOmbN9BoMBGo0Ger0earXaZfv1ZDs/EfmW0u/PYuo7u22WWzdrlEearRz5/GqrZsv8bk1okM3lPJx17f1pK5A9m5mA328+YnMovuK//8NvUNcLUADf/j6jQ32EHPn+Zo2QB8ht0UYiT/CXPzi8acRpR/op2TOXWmeFIKD1/Wmv83dAgAJz1uxvd39C+h8yCwDgivqxWbf2dVtHaYBBiIj8kDd0LHYVbxlxam8/pbYCqK251DqbtfvT1h+lEwdHY8bN8S5pPpMTV4Sg8YMikfOrRBfsyX4MQkTkV/ytY7F5xGlbIcIdI07t7adkMolWzUphIUF49OZ4xPXs2mnnZ4smpAtMQuDj8h/srh1MS9QyCHnAnupzMJqEW2tvGYSIyG/4W8di4GqtxV3DovHXHdVtlunsEaf2zoz/xNqvW22rb2jGyuIqdFUFdtr52XLFJPDg3/ZIz23VDpoXb9UEd4GeM0u71U+NV7D72FncPCDCbcfkzNJE5Df8cSmbwoo6vN1OCJo9pm+n13C5ov/RxUZju9sVuDrM3fzfrnTtsdtb6Lrl4q0MQZ6x6+gZtx6PQYiI/IY3dSx2hfZquICrgeGTb+o6fbFmd8x4L3B1zbK3HkqCVtO5x2troWtzs6on+zIR8EN9g1uPxyBERH7DWzoWu4q31HDZWvzUlSYOjsbOJeOwbtYozB3b3+n92GopvPbeNV0x4bf/OsiBYF4gJizErcdjECIitzKaBEq/P4uPy39A6fdn263NcKQsYP9q5b6ylI231HDZs/hpR5n7b5k7yqb274mnbh/odACzt5Ls9IXLKKyow6gVJTh3sfOG75P93Nk/CGBnaSJyI0eGtTszBN7flrLxphqu9mbGfzYzEb/ffLhDTUota2jMQ9pb/nt2lpozl/BK8XesCfIS4aFBGNXPvfPscWbpdnTWzNJEcmRrVuGWw9odKdvWsfxhHiGjSeCWP261uVjzziXjOjXctZwbKKKrClAAZ35qtBiKXlhRZ3MSQnu8ev9w3DE0xmIuovMXm5wKWj26BuH8xeZ2+1h15mzW5Lg37h+BO4bHdHg/nFmaiLyKI8Pa8d//7sgQ+PZmDfYl9tRw3f+LOHx24GSnXWN7obLlZIQTB0fjrYeS8PRHBzsULKp/vIhfvFCMcxebLI73bGYCwruqoDNcxpkLjXhja1Wbo7rMAfHZzARkWxnSbybQubNZk+NOeWAgA2uE2sEaISLXcGS9LABetbaWN7AWRsJDg1p9kbu61qvgwEmrcwOZQ9lTadcjPqKrRQgzmgTmrf8anx1oPTS9PQoAocpAXGyyPsxeAeu1hkDrlS5ali04UIe56/bLfhFTX/FIah/8btLgDu+HNUJE5FU6o9OvrwyBd4Vra7hqzlzEyuKqVuVcOXv21QBhvTbFnClanoM5hN2eqEXZ8fMOH08AbYYg8/aWNYHmPkvWaqA0/52PCADCuyoZgnzIdW4eMQZw1BgRuYEjnX69qYOwNzGPpLpjaAzW7zthtUxb8+M4qrCiDk+sdawWpU5/GXPW7MfrJVVO9eWxZ+bpOv1l7D52VnpuMllv2tJfapYmTJRTYPYHwgOplUHIDzk65JioszkyrN3fhsC7WmfPLWTuz+WsV0ta11TZMjK+p82Zp82y/3414BQcOIm566x3zm4ZCHuEKB0+H/KcgkOONam6ApvG/Iy/jJYh/+LosHZ/GgLvap3RzNhyVNiZC40dHgbvqMJDOrvL1jc02zU6zRwI/9+GtjtLA7b7JpF7fVt3we2LrrJGyI+0NT18e+vqELmLuU/HtcsnaDXBrfq0OFLW13S0xtbVTYfmtbWmvrMb89aX4/ebjzh0Pt7uvI1RYQKA4CxCXqPJKNy+FiBrhPyEP666Tf7H1rB2o0lg9/dnUXrsDAAFXpo8FAEKBc5cbPTZIfAtuaLG1tx0aGtuIXuaDtuar0luLjWZPH0K1MJJN681xiDkJxzpNyCXIcfkncydfq9VWFHXagTQG9uAsNAgvHjPEJ//uW0rdDg60stVs2fbWtDVFcJCglDfwHl6yDFf157H5OTebjueQ01j8fHxUCgUrR7Z2dmoqamxuk2hUGDjxo2t9nX27Fn07t0bCoUC9fX1Ftu2b9+OpKQkqFQqDBgwAPn5+a3ev2rVKsTHxyM4OBgpKSnYu3evxfbLly8jOzsbPXv2RLdu3TB58mScOnXKkcv1Kd6yJhGRM8yzElsbAVR/6WqfEF9u2rVVYws4NtLLFU2Htv54coVVDyT9d/HUAXaVfyrtekR38srz5P0O/KfercdzKAjt27cPdXV10qOoqAgAMGXKFMTGxlpsq6urw/PPP49u3bohIyOj1b5mzJiBoUOHtnq9uroamZmZGDt2LMrLyzF//nzMnDkTW7Zskcps2LABCxYswPLly7F//34MGzYM6enpOH36tFTmqaeewqeffoqNGzfi888/x8mTJ3HPPfc4crk+hUOOyVcZTQLPfXLIZrmODgn3pI6O9LLWr6jlKu0r7x2GZzMTsHjiIGhClHbdJ2f+KIrWBOOth5Lw5gNJNld3D/vvXD4j+/bAU7ffYNdIwLnjrsfOJePw95kpCAsJaqM0+buq0z+59XfdoaaxXr16WTx/8cUX0b9/f9x2221QKBTQarUW2//1r3/h3nvvRbdu3SxeX716Nerr67Fs2TL8+9//ttj21ltvoW/fvvjzn/8MAEhISMDOnTuxcuVKpKenAwD+8pe/YNasWXj00Uel92zevBnvvvsunn76aej1euTm5mLt2rUYN24cACAvLw8JCQnYvXs3Ro0a5chl+wRX9hsgcqe91eegMzTaLOfLTbsdqbG11a9I39CEl7ZUOtzvyN4/ip7NTEBEd1WrPlpvYITVWafN6i8148HcPdK5ONKcd/OACLw4eQj7L8lUQ7PJrb/rTo8aa2pqwpo1a5CVlQWFonXOLysrQ3l5OWbMmGHx+uHDh/G73/0O77//PgICWh++tLQUaWlpFq+lp6ejtLRUOm5ZWZlFmYCAAKSlpUllysrK0NzcbFFm0KBBiIuLk8pY09jYCIPBYPHwFeZ+AwBa/dXFIcfkzeQwm7SzNbZtjQQ1T144d+1+p0eK2jtf0/Sb+2LS8OuQ2r+nxefHr4bG4K2Hkmw2ZZnPBUCbzXnz025A4xWTxSi6iYOjseqBETZrnlrq0ZW1SP7Cnb/rTgehTZs2ob6+HtOnT7e6PTc3FwkJCRg9erT0WmNjI6ZOnYqXX34ZcXFxVt+n0+kQFRVl8VpUVBQMBgMaGhpw5swZGI1Gq2V0Op20D6VSibCwsDbLWLNixQpoNBrpERsb22ZZb+TPQ47JfznSXOurTbvOTBJpT2fmzw7UOd3vyBV/PJmb59prymp5LrcnaqXmvFfvH46n0q6HEAIri7/DvPXlmPrObtzyx61SgAvvqrJrduu5Ywdg3axR2J2TJjXJkW9z5++600EoNzcXGRkZiImJabWtoaEBa9eubVUblJOTg4SEBDz00EPOHrZT5eTkQK/XS48TJ6xPY+/NWvYbePX+4Vg3axR2LhnHEERea2TfHtCqVTbL+fJs0s6Ejo52ZrZnhmlX/PEUGKBAgELR7uiwlucSGKDAyL49pPXSrm0WbVmbZW+twPVR3aRmlCtGDoX3dT26Kt36u+7U8Pnjx4+juLgYH330kdXtH374IS5duoRHHnnE4vWtW7fi4MGD+PDDDwEA5oXvIyIi8Mwzz+D555+HVqttNbrr1KlTUKvVCAkJQWBgIAIDA62WMfdR0mq1aGpqQn19vUWtUMsy1qhUKqhUtj+QvV1bw5NdoeUMtP4wrwt5XmCAAs/ddaPN2YJ9vWnXHDqu7e+jbaM/j6uaBmztx9bcTq44RstyhRV1eO6Tw9AZrL+n5bxnL01uPaDGmoiuKhRW1OG3/6rAT3Yu1UHeK8XN3ytOBaG8vDxERkYiMzPT6vbc3FzcddddrTpX//Of/0RDw88TJe3btw9ZWVn44osv0L9/fwBAamoqCgoKLN5XVFSE1NRUAIBSqURycjJKSkpw9913AwBMJhNKSkowd+5cAEBycjKCgoJQUlKCyZMnAwAqKytRW1sr7Yccx+U7COicMDxxcDTeamMl8fDQIKy4Z4hf/Iw5Ejpc1TRgz346+seTvedac+YSXin+zmYHaHMN0pE6+/pp7qs5h1dLqtix2k/079XVrcdzOAiZTCbk5eVh2rRp6NKl9duPHj2KHTt2tAozAKSwY3bmzBkAV0eGmWtu5syZgzfeeAOLFy9GVlYWtm7din/84x/YvHmz9L4FCxZg2rRpuOmmmzBy5Ei88soruHjxojSKTKPRYMaMGViwYAF69OgBtVqNJ598EqmpqX45YswdXDUZHPm2zgzD5pDQcmbp1P49MapfT4/UBHVW7ae9ocPWSFBbHBkp2tFrtWfUapRahXV7ax26lleK7VvANX9XjU+EoF8N1mJ/7Xm7RknKWUq8e0eGOhyEiouLUVtbi6ysLKvb3333XfTu3RsTJkxw6oT69u2LzZs346mnnsKrr76K3r17429/+5s0dB4A7rvvPvz4449YtmwZdDodhg8fjsLCQosO1CtXrkRAQAAmT56MxsZGpKen480333TqnOROTst3sOmvbe4Iw4EBCtx8fQRuvj6iQ/vpKG+o/Ww5g7SjHBkp6oprtWe266kj47DSzmBjdqnZvmYuX5m9On2wFncNj7Fr0VhZc/NHrkKYO+pQKwaDARqNBnq9Hmq12tOn4zGl35/F1Hd22yy3btYon5zjxcwbvvy8ldEkcMsft7bZeddc+7BzyTifD45tBT7zVbm79tNWnxpr7P25dfW1tvc71HjFhHnry+3elz8yf0Y+seYrFFT470oHHZUxWIvVDyV3aB+OfH9zrTGySQ7Ld7Dpr31yWcvOG2s/Jw6ORndVEB7M3WOz7NyxA3DzgAi7ajI741onDo7GuEFR+KC0BsfPXUKfHqF4ODUeyi4BKP3+rF378IRABWDs5CoBdXAXJPcJR+n3Z9Gnp3v7wPiaHd/9CKNJuO13jEGIbPL35Tu88cvP28ghDAPeG/jOXLSvT0nLYeS2dMa1WqsR+tvOaiy/MxG3J2o71OepM8wd2x+p/SMw/d09nR6E+kZ0xcgXin2mGc+TLjYZfWNmaZIPZyaD8yUdXQcKsL4WlD/x9zBs5q2BrzPuv6uvta1ZsM21qkWHdW3OpeQp10d1x1c159HshqmHvvmPniHIAT4xszTJh78v39HRL4TCijrc8setmPrObquz4/oDfw/DZt4W+MwBW6dvQI+uSpfef1deq61aVeDnWlVrEzh66qMjopsKeV9We+bg1C53/lHFpjGyi6OTwfmSjnwhyKVvkT2jgnw5DJt50+LF1pqZrHH2/rvyWh2pVbU2l9L5i43I/u8Cru6qSw0PDQKE74w4k5PuwYHeP7M0yZMrZqD1Rs5+Ifhj36L2pg/w5zBs5i2Br62AbY2z99+V1+poraq1uZRWByha/WwFKGDXWmPOEABO/8T5fLxRcly4988sTfLVmct3eIqzXwje2rHWWfZMH+CvYbglTwc+exZb7dE1CM/ecSO06o7df1ddqyua2azXFDUhe+3VOXdcnYfqLzWj4MBJF++VXOHW63vZLuRCDEJEcO4LwVs71jrDkSY+fwzD1/Jk4LNnsdVzF5uhVQe75N/BFdfqTK1qW7WPrWuKWv9eukrRkdMu3yd13MOp8W49HoMQ0X85+oXgbR1rneWPTXxtcWTmcE8FPk8EbGvX6ui9Wn5nYpszJgtY1qo6Mnnp7YladFcFYWPZCWwqZw2OHGz99pRbm9oZhIhacOTLz5s61naEvzXxtcVXZg73hoDdmffKkdpHezuMk39x9x9eHD5P5CR/mVbAn5r42mJrjhtvmurA01MVOHOvzLWKbTHXKjZdMdk1zN5oEm2eB/k/W/O2uRqDEFEHmPsWXTsvilYT7DND572hBqIz2TvHjbdMgtlewAaunvOzmQmdErCdvVf21ip+UFpjV7nd35+12WGc/Js7//Bi0xh5PW9fEd7XR1L5SxNfW3yx6a+tzvtmv998BAEBCpcHbWfvlb1fWsfPXbKrXOmxM51SE6QMAJrcMIs0dRwnVCT6L1/p1+HLI6m8Ze6czuKrTX8TB0fDZAKeWNu6A3JnTdjp7L2qOXPRrvf16RFq55m4/metm6oLdueMx9Dnt3Ta3ETkGu6eUJFNY+S1fKlfh6/zhya+tvhq05/RJPD7zdb73XRWk54z98poEli3t9bme6I1wXg4Nd6u/k+d8UfFn6YMRbfgLph1a1+X75tc63+SenNCRSI5Den2Fr7exNcWX23680STnjP3am/1OegMtmdovv8XcVB2CbCr9nFUv54uW6leAWDVAz+H+ZxfXe1/9faOavZB8lI/Xmhy6/FYI0ReyRUrwpPjzE18k4Zfh9T+PX0+BAG+O7rPU/MJOXqv7D1+fMTVZjF7ah9tdRh3hAAQ3lVp8VrOrxJR+b8Z+J+k6zq4d+oMmyvq0HTFfZ25GITIK/lqvw7yTr7Y9OepJj1H75Uz5zlxcDR2LhmHdbNG4dX7h2PdrFHYuWScxb7bOo+w0CAAjgUka58Tyi4B+NO9w5F1c7wDeyJ3EAL4oLTGbcdj0xh5JV/t10Hey9ea/jzZpOfIvXL2PO0ZYNDWeRQd1jk00WJ7nxO3J2rx7pc1du3HWwQFKtBs9O+GPXtHGLoCgxB5JV/t10HezZdG93l6NJ+996qzz9PaeZgD0u5jZ5H99/2ob2i2+l57PidsfdZ4m26qLph5S1+8UlLl6VPpVPaPMOw4No2RV/LVfh1EruQrTXqeOM/AAAVuHhCBFycPgQLOf064sj+SO7w0eSieHH+91ETojwIU7l14VSGE8IUQ7BEGgwEajQZ6vR5qtdrTpyNLvjKPEFFn8vZJRc08dZ6u+Jxwx7pmCgDz026AvqEJa/fU4rKDHYIfG9NXGvVWWFHX5iK3vq7ldTrLke9vBqF2MAh5B1/5EiAiz3HF58S1+zh/sQm/3XQQ9ZesN70BV2sv7JnK6dpgZjQJvF5Shb/tPIafGo3tvrdLgAKv3T8cvxoaY/F6Z4S3UGUgLjW1fz6dJUABzLq14yEIYBByGQYhIiJ5M5oEdh87i9LvzwIQSOnbEwEKBc5cbERk92Ak9wlH2fHzKDqsw6bykzh38ec5cHp2VWLS8BjcnqhtM5iZw9ffvvgeW7/9sVUfq8wh0Xh16og2Q13L8BbRTQUI4MzFRkR0VQEK4MxPlv8d2T0YZy80YtmnhyzO1RzUbk/U4o2tVcj7ssai71W0JhjPZiYivKsSpy9cRs2ZS8jbVd1uSGypqzIQM2/ti+yx12NfzTmUfn8WRpMJFy43Q6EIQHzPUDycGg9lF9f02GEQchEGISIisldHa6WarpjwQWkNjp+7hD49XBsMHD1Xe66lvZB4bfhyd00+g5CLMAgRERH5Hke+vzlqjIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGTLoSAUHx8PhULR6pGdnY2amhqr2xQKBTZu3AgA+OabbzB16lTExsYiJCQECQkJePXVV1sdZ/v27UhKSoJKpcKAAQOQn5/fqsyqVasQHx+P4OBgpKSkYO/evRbbL1++jOzsbPTs2RPdunXD5MmTcerUKUcul4iIiPycQ0Fo3759qKurkx5FRUUAgClTpiA2NtZiW11dHZ5//nl069YNGRkZAICysjJERkZizZo1OHToEJ555hnk5OTgjTfekI5RXV2NzMxMjB07FuXl5Zg/fz5mzpyJLVu2SGU2bNiABQsWYPny5di/fz+GDRuG9PR0nD59Wirz1FNP4dNPP8XGjRvx+eef4+TJk7jnnns6dLOIiIjIvyiEEMLZN8+fPx+fffYZqqqqoFAoWm0fMWIEkpKSkJub2+Y+srOzceTIEWzduhUAsGTJEmzevBkVFRVSmfvvvx/19fUoLCwEAKSkpOAXv/iFFKBMJhNiY2Px5JNP4umnn4Zer0evXr2wdu1a/M///A8A4Ntvv0VCQgJKS0sxatQou67PYDBAo9FAr9dDrVbbd1OIiIjIoxz5/na6j1BTUxPWrFmDrKwsqyGorKwM5eXlmDFjRrv70ev16NGjh/S8tLQUaWlpFmXS09NRWloqHbesrMyiTEBAANLS0qQyZWVlaG5utigzaNAgxMXFSWWsaWxshMFgsHgQERGR/3I6CG3atAn19fWYPn261e25ublISEjA6NGj29zHrl27sGHDBsyePVt6TafTISoqyqJcVFQUDAYDGhoacObMGRiNRqtldDqdtA+lUomwsLA2y1izYsUKaDQa6REbG9tmWSIiIvJ9Tgeh3NxcZGRkICYmptW2hoYGrF27tt3aoIqKCkyaNAnLly/HhAkTnD0Nl8rJyYFer5ceJ06c8PQpERERUSfq4sybjh8/juLiYnz00UdWt3/44Ye4dOkSHnnkEavbDx8+jPHjx2P27NlYunSpxTatVttqdNepU6egVqsREhKCwMBABAYGWi2j1WqlfTQ1NaG+vt6iVqhlGWtUKhVUKlWb24mIiMi/OFUjlJeXh8jISGRmZlrdnpubi7vuugu9evVqte3QoUMYO3Yspk2bhhdeeKHV9tTUVJSUlFi8VlRUhNTUVACAUqlEcnKyRRmTyYSSkhKpTHJyMoKCgizKVFZWora2VipDRERE5HCNkMlkQl5eHqZNm4YuXVq//ejRo9ixYwcKCgpabauoqMC4ceOQnp6OBQsWSP11AgMDpdA0Z84cvPHGG1i8eDGysrKwdetW/OMf/8DmzZul/SxYsADTpk3DTTfdhJEjR+KVV17BxYsX8eijjwIANBoNZsyYgQULFqBHjx5Qq9V48sknkZqaaveIMSIiIpIB4aAtW7YIAKKystLq9pycHBEbGyuMRmOrbcuXLxcAWj369OljUW7btm1i+PDhQqlUin79+om8vLxW+3r99ddFXFycUCqVYuTIkWL37t0W2xsaGsQTTzwhwsPDRWhoqPj1r38t6urqHLpWvV4vAAi9Xu/Q+4iIiMhzHPn+7tA8Qv6O8wgRERH5HrfMI0RERETk6xiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYcCkLx8fFQKBStHtnZ2aipqbG6TaFQYOPGjdI+amtrkZmZidDQUERGRmLRokW4cuWKxXG2b9+OpKQkqFQqDBgwAPn5+a3OZdWqVYiPj0dwcDBSUlKwd+9ei+2XL19GdnY2evbsiW7dumHy5Mk4deqUI5dLREREfs6hILRv3z7U1dVJj6KiIgDAlClTEBsba7Gtrq4Ozz//PLp164aMjAwAgNFoRGZmJpqamrBr1y689957yM/Px7Jly6RjVFdXIzMzE2PHjkV5eTnmz5+PmTNnYsuWLVKZDRs2YMGCBVi+fDn279+PYcOGIT09HadPn5bKPPXUU/j000+xceNGfP755zh58iTuueeeDt0sIiIi8jOiA+bNmyf69+8vTCaT1e3Dhw8XWVlZ0vOCggIREBAgdDqd9Nrq1auFWq0WjY2NQgghFi9eLG688UaL/dx3330iPT1dej5y5EiRnZ0tPTcajSImJkasWLFCCCFEfX29CAoKEhs3bpTKHDlyRAAQpaWldl+fXq8XAIRer7f7PURERORZjnx/O91HqKmpCWvWrEFWVhYUCkWr7WVlZSgvL8eMGTOk10pLSzFkyBBERUVJr6Wnp8NgMODQoUNSmbS0NIt9paeno7S0VDpuWVmZRZmAgACkpaVJZcrKytDc3GxRZtCgQYiLi5PKWNPY2AiDwWDxICIiIv/ldBDatGkT6uvrMX36dKvbc3NzkZCQgNGjR0uv6XQ6ixAEQHqu0+naLWMwGNDQ0IAzZ87AaDRaLdNyH0qlEmFhYW2WsWbFihXQaDTSIzY2tu0bQERERD7P6SCUm5uLjIwMxMTEtNrW0NCAtWvXWtQG+YKcnBzo9XrpceLECU+fEhEREXWiLs686fjx4yguLsZHH31kdfuHH36IS5cu4ZFHHrF4XavVthrdZR7JpdVqpf+/dnTXqVOnoFarERISgsDAQAQGBlot03IfTU1NqK+vt6gValnGGpVKBZVK1c6VExERkT9xqkYoLy8PkZGRyMzMtLo9NzcXd911F3r16mXxempqKg4ePGgxuquoqAhqtRqJiYlSmZKSEov3FRUVITU1FQCgVCqRnJxsUcZkMqGkpEQqk5ycjKCgIIsylZWVqK2tlcoQEREROTxqzGg0iri4OLFkyRKr26uqqoRCoRD//ve/W227cuWKGDx4sJgwYYIoLy8XhYWFolevXiInJ0cqc+zYMREaGioWLVokjhw5IlatWiUCAwNFYWGhVGb9+vVCpVKJ/Px8cfjwYTF79mwRFhZmMRptzpw5Ii4uTmzdulV89dVXIjU1VaSmpjp0rRw1RkRE5Hsc+f52OAht2bJFABCVlZVWt+fk5IjY2FhhNBqtbq+pqREZGRkiJCREREREiIULF4rm5maLMtu2bRPDhw8XSqVS9OvXT+Tl5bXaz+uvvy7i4uKEUqkUI0eOFLt377bY3tDQIJ544gkRHh4uQkNDxa9//WtRV1fn0LUyCBEREfkeR76/FUII4dEqKS9mMBig0Wig1+uhVqs9fTpERERkB0e+v7nWGBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJlkNBKD4+HgqFotUjOztbKlNaWopx48aha9euUKvVGDNmDBoaGqTt3333HSZNmoSIiAio1Wrccsst2LZtm8VxamtrkZmZidDQUERGRmLRokW4cuWKRZnt27cjKSkJKpUKAwYMQH5+fqvzXbVqFeLj4xEcHIyUlBTs3bvXkcslIiIiP+dQENq3bx/q6uqkR1FREQBgypQpAK6GoIkTJ2LChAnYu3cv9u3bh7lz5yIg4OfD3HHHHbhy5Qq2bt2KsrIyDBs2DHfccQd0Oh0AwGg0IjMzE01NTdi1axfee+895OfnY9myZdI+qqurkZmZibFjx6K8vBzz58/HzJkzsWXLFqnMhg0bsGDBAixfvhz79+/HsGHDkJ6ejtOnTzt/t4iIiMi/iA6YN2+e6N+/vzCZTEIIIVJSUsTSpUvbLP/jjz8KAGLHjh3SawaDQQAQRUVFQgghCgoKREBAgNDpdFKZ1atXC7VaLRobG4UQQixevFjceOONFvu+7777RHp6uvR85MiRIjs7W3puNBpFTEyMWLFihd3Xp9frBQCh1+vtfg8RERF5liPf3073EWpqasKaNWuQlZUFhUKB06dPY8+ePYiMjMTo0aMRFRWF2267DTt37pTe07NnTwwcOBDvv/8+Ll68iCtXruCvf/0rIiMjkZycDOBqrdKQIUMQFRUlvS89PR0GgwGHDh2SyqSlpVmcT3p6OkpLS6VzKysrsygTEBCAtLQ0qYw1jY2NMBgMFg8iIiLyX04HoU2bNqG+vh7Tp08HABw7dgwA8Nxzz2HWrFkoLCxEUlISxo8fj6qqKgCAQqFAcXExvv76a3Tv3h3BwcH4y1/+gsLCQoSHhwMAdDqdRQgCID03N5+1VcZgMKChoQFnzpyB0Wi0Wsa8D2tWrFgBjUYjPWJjY528O0REROQLnA5Cubm5yMjIQExMDADAZDIBAB577DE8+uijGDFiBFauXImBAwfi3XffBQAIIZCdnY3IyEh88cUX2Lt3L+6++27ceeedqKurc8HldExOTg70er30OHHihKdPiYiIiDpRF2fedPz4cRQXF+Ojjz6SXouOjgYAJCYmWpRNSEhAbW0tAGDr1q347LPPcP78eajVagDAm2++iaKiIrz33nt4+umnodVqW43uOnXqFABAq9VK/29+rWUZtVqNkJAQBAYGIjAw0GoZ8z6sUalUUKlUdt8HIiIi8m1O1Qjl5eUhMjISmZmZ0mvx8fGIiYlBZWWlRdnvvvsOffr0AQBcunTp6kEDLA8bEBAg1Silpqbi4MGDFqO7ioqKoFarpZCVmpqKkpISi30UFRUhNTUVAKBUKpGcnGxRxmQyoaSkRCpDRERE5PCoMaPRKOLi4sSSJUtabVu5cqVQq9Vi48aNoqqqSixdulQEBweLo0ePCiGujhrr2bOnuOeee0R5ebmorKwUv/nNb0RQUJAoLy8XQghx5coVMXjwYDFhwgRRXl4uCgsLRa9evUROTo50nGPHjonQ0FCxaNEiceTIEbFq1SoRGBgoCgsLpTLr168XKpVK5Ofni8OHD4vZs2eLsLAwi9FotnDUGBERke9x5Pvb4SC0ZcsWAUBUVlZa3b5ixQrRu3dvERoaKlJTU8UXX3xhsX3fvn1iwoQJokePHqJ79+5i1KhRoqCgwKJMTU2NyMjIECEhISIiIkIsXLhQNDc3W5TZtm2bGD58uFAqlaJfv34iLy+v1bm8/vrrIi4uTiiVSjFy5Eixe/duh66VQYiIiMj3OPL9rRBCCI9WSXkxg8EAjUYDvV4v9WkiIiIi7+bI9zfXGiMiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2XIoCMXHx0OhULR6ZGdnS2VKS0sxbtw4dO3aFWq1GmPGjEFDQ4PFfjZv3oyUlBSEhIQgPDwcd999t8X22tpaZGZmIjQ0FJGRkVi0aBGuXLliUWb79u1ISkqCSqXCgAEDkJ+f3+p8V61ahfj4eAQHByMlJQV79+515HKJiIjIzzkUhPbt24e6ujrpUVRUBACYMmUKgKshaOLEiZgwYQL27t2Lffv2Ye7cuQgI+Pkw//znP/Hwww/j0UcfxTfffIMvv/wSDzzwgLTdaDQiMzMTTU1N2LVrF9577z3k5+dj2bJlUpnq6mpkZmZi7NixKC8vx/z58zFz5kxs2bJFKrNhwwYsWLAAy5cvx/79+zFs2DCkp6fj9OnTzt0pIiIi8j+iA+bNmyf69+8vTCaTEEKIlJQUsXTp0jbLNzc3i+uuu0787W9/a7NMQUGBCAgIEDqdTnpt9erVQq1Wi8bGRiGEEIsXLxY33nijxfvuu+8+kZ6eLj0fOXKkyM7Olp4bjUYRExMjVqxYYff16fV6AUDo9Xq730NERESe5cj3t9N9hJqamrBmzRpkZWVBoVDg9OnT2LNnDyIjIzF69GhERUXhtttuw86dO6X37N+/Hz/88AMCAgIwYsQIREdHIyMjAxUVFVKZ0tJSDBkyBFFRUdJr6enpMBgMOHTokFQmLS3N4nzS09NRWloqnVtZWZlFmYCAAKSlpUlliIiIiJwOQps2bUJ9fT2mT58OADh27BgA4LnnnsOsWbNQWFiIpKQkjB8/HlVVVa3KLF26FJ999hnCw8Pxy1/+EufOnQMA6HQ6ixAEQHqu0+naLWMwGNDQ0IAzZ87AaDRaLWPehzWNjY0wGAwWDyIiIvJfTgeh3NxcZGRkICYmBgBgMpkAAI899hgeffRRjBgxAitXrsTAgQPx7rvvWpR55plnMHnyZCQnJyMvLw8KhQIbN27s6LV02IoVK6DRaKRHbGysp0+JiIiIOpFTQej48eMoLi7GzJkzpdeio6MBAImJiRZlExISUFtb22YZlUqFfv36SWW0Wi1OnTplsQ/zc61W224ZtVqNkJAQREREIDAw0GoZ8z6sycnJgV6vlx4nTpywcSeIiIjIlzkVhPLy8hAZGYnMzEzptfj4eMTExKCystKi7HfffYc+ffoAAJKTk6FSqSzKNDc3o6amRiqTmpqKgwcPWozuKioqglqtlgJUamoqSkpKLI5TVFSE1NRUAIBSqURycrJFGZPJhJKSEqmMNSqVCmq12uJBREREfszRnthGo1HExcWJJUuWtNq2cuVKoVarxcaNG0VVVZVYunSpCA4OFkePHpXKzJs3T1x33XViy5Yt4ttvvxUzZswQkZGR4ty5c0IIIa5cuSIGDx4sJkyYIMrLy0VhYaHo1auXyMnJkfZx7NgxERoaKhYtWiSOHDkiVq1aJQIDA0VhYaFUZv369UKlUon8/Hxx+PBhMXv2bBEWFmYxGs0WjhojIiLyPY58fzschLZs2SIAiMrKSqvbV6xYIXr37i1CQ0NFamqq+OKLLyy2NzU1iYULF4rIyEjRvXt3kZaWJioqKizK1NTUiIyMDBESEiIiIiLEwoULRXNzs0WZbdu2ieHDhwulUin69esn8vLyWp3L66+/LuLi4oRSqRQjR44Uu3fvduhaGYSIiIh8jyPf3wohhPBolZQXMxgM0Gg00Ov1bCYjIiLyEY58f3OtMSIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki2HglB8fDwUCkWrR3Z2tlSmtLQU48aNQ9euXaFWqzFmzBg0NDS02ldjYyOGDx8OhUKB8vJyi20HDhzArbfeiuDgYMTGxuKll15q9f6NGzdi0KBBCA4OxpAhQ1BQUGCxXQiBZcuWITo6GiEhIUhLS0NVVZUjl0tERER+zqEgtG/fPtTV1UmPoqIiAMCUKVMAXA1BEydOxIQJE7B3717s27cPc+fORUBA68MsXrwYMTExrV43GAyYMGEC+vTpg7KyMrz88st47rnn8Pbbb0tldu3ahalTp2LGjBn4+uuvcffdd+Puu+9GRUWFVOall17Ca6+9hrfeegt79uxB165dkZ6ejsuXLztyyUREROTPRAfMmzdP9O/fX5hMJiGEECkpKWLp0qU231dQUCAGDRokDh06JACIr7/+Wtr25ptvivDwcNHY2Ci9tmTJEjFw4EDp+b333isyMzMt9pmSkiIee+wxIYQQJpNJaLVa8fLLL0vb6+vrhUqlEuvWrbP7+vR6vQAg9Hq93e8hIiIiz3Lk+9vpPkJNTU1Ys2YNsrKyoFAocPr0aezZsweRkZEYPXo0oqKicNttt2Hnzp0W7zt16hRmzZqFDz74AKGhoa32W1paijFjxkCpVEqvpaeno7KyEufPn5fKpKWlWbwvPT0dpaWlAIDq6mrodDqLMhqNBikpKVIZaxobG2EwGCweRERE5L+cDkKbNm1CfX09pk+fDgA4duwYAOC5557DrFmzUFhYiKSkJIwfP17qmyOEwPTp0zFnzhzcdNNNVver0+kQFRVl8Zr5uU6na7dMy+0t32etjDUrVqyARqORHrGxsTbvAxEREfkup4NQbm4uMjIypH4+JpMJAPDYY4/h0UcfxYgRI7By5UoMHDgQ7777LgDg9ddfx4ULF5CTk+OCU3e9nJwc6PV66XHixAlPnxIRERF1IqeC0PHjx1FcXIyZM2dKr0VHRwMAEhMTLcomJCSgtrYWALB161aUlpZCpVKhS5cuGDBgAADgpptuwrRp0wAAWq0Wp06dstiH+blWq223TMvtLd9nrYw1KpUKarXa4kFERET+y6kglJeXh8jISGRmZkqvxcfHIyYmBpWVlRZlv/vuO/Tp0wcA8Nprr+Gbb75BeXk5ysvLpSHvGzZswAsvvAAASE1NxY4dO9Dc3Czto6ioCAMHDkR4eLhUpqSkxOI4RUVFSE1NBQD07dsXWq3WoozBYMCePXukMkREREQOjxozGo0iLi5OLFmypNW2lStXCrVaLTZu3CiqqqrE0qVLRXBwsDh69KjVfVVXV7caNVZfXy+ioqLEww8/LCoqKsT69etFaGio+Otf/yqV+fLLL0WXLl3En/70J3HkyBGxfPlyERQUJA4ePCiVefHFF0VYWJj4+OOPxYEDB8SkSZNE3759RUNDg93XylFjREREvseR7+8ujgan4uJi1NbWIisrq9W2+fPn4/Lly3jqqadw7tw5DBs2DEVFRejfv7/d+9doNPi///s/ZGdnIzk5GREREVi2bBlmz54tlRk9ejTWrl2LpUuX4re//S2uv/56bNq0CYMHD5bKLF68GBcvXsTs2bNRX1+PW265BYWFhQgODnb0komIiMhPKYQQwtMn4a0MBgM0Gg30ej37CxEREfkIR76/udYYERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyZbDa40RERER2cNoEthbfQ6nL1xGZPdgjOzbA4EBCk+flgUGISIiInK5woo6PP/pYdTpL0uvRWuCsfzOREwcHO3BM7PEpjEiIiJyqcKKOjy+Zr9FCAIAnf4yHl+zH4UVdR46s9YYhIiIiMhljCaB5z89DGFlm/m15z89DKPJWgn3YxAiIiIil9lbfa5VTVBLAkCd/jL2Vp9z30m1g0GIiIiIXOb0hbZDkDPlOhuDEBEREblMZPdgl5brbAxCRERE5DIj+/ZAtCYYbQ2SV+Dq6LGRfXu487TaxCBERERELhMYoMDyOxMBoFUYMj9ffmei18wnxCBERERELjVxcDRWP5QErcay+UurCcbqh5K8ah4hTqhIRERELjdxcDRuT9RyZmkiIiKSp8AABVL79/T0abSLTWNEREQkWwxCREREJFsMQkRERCRbDEJEREQkWwxCREREJFsMQkRERCRbDEJEREQkWwxCREREJFsMQkRERCRbnFm6HUIIAIDBYPDwmRAREZG9zN/b5u/x9jAItePChQsAgNjYWA+fCRERETnqwoUL0Gg07ZZRCHvikkyZTCacPHkS3bt3h0JhuUicwWBAbGwsTpw4AbVa7aEz9E+8t52L97fz8N52Lt7fzuNv91YIgQsXLiAmJgYBAe33AmKNUDsCAgLQu3fvdsuo1Wq/+KHxRry3nYv3t/Pw3nYu3t/O40/31lZNkBk7SxMREZFsMQgRERGRbDEIOUmlUmH58uVQqVSePhW/w3vbuXh/Ow/vbefi/e08cr637CxNREREssUaISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItvw1Czz33HBQKhcVj0KBB0vbLly8jOzsbPXv2RLdu3TB58mScOnXKYh+1tbXIzMxEaGgoIiMjsWjRIly5csWizPbt25GUlASVSoUBAwYgPz+/1bmsWrUK8fHxCA4ORkpKCvbu3Wux3Z5z8Sa27u0vf/nLVtvnzJljsQ/e27b98MMPeOihh9CzZ0+EhIRgyJAh+Oqrr6TtQggsW7YM0dHRCAkJQVpaGqqqqiz2ce7cOTz44INQq9UICwvDjBkz8NNPP1mUOXDgAG699VYEBwcjNjYWL730Uqtz2bhxIwYNGoTg4GAMGTIEBQUFFtvtORdvY+v+Tp8+vdXP78SJEy32wfvbWnx8fKv7plAokJ2dDYCfuR1l6/7yc7cDhJ9avny5uPHGG0VdXZ30+PHHH6Xtc+bMEbGxsaKkpER89dVXYtSoUWL06NHS9itXrojBgweLtLQ08fXXX4uCggIREREhcnJypDLHjh0ToaGhYsGCBeLw4cPi9ddfF4GBgaKwsFAqs379eqFUKsW7774rDh06JGbNmiXCwsLEqVOn7D4Xb2Pr3t52221i1qxZFtv1er20nfe2befOnRN9+vQR06dPF3v27BHHjh0TW7ZsEUePHpXKvPjii0Kj0YhNmzaJb775Rtx1112ib9++oqGhQSozceJEMWzYMLF7927xxRdfiAEDBoipU6dK2/V6vYiKihIPPvigqKioEOvWrRMhISHir3/9q1Tmyy+/FIGBgeKll14Shw8fFkuXLhVBQUHi4MGDDp2LN7Hn/k6bNk1MnDjR4uf33LlzFvvh/W3t9OnTFvesqKhIABDbtm0TQvAzt6Ns3V9+7jrPr4PQsGHDrG6rr68XQUFBYuPGjdJrR44cEQBEaWmpEEKIgoICERAQIHQ6nVRm9erVQq1Wi8bGRiGEEIsXLxY33nijxb7vu+8+kZ6eLj0fOXKkyM7Olp4bjUYRExMjVqxYYfe5eJv27q0QV38h582b1+Z23tu2LVmyRNxyyy1tbjeZTEKr1YqXX35Zeq2+vl6oVCqxbt06IYQQhw8fFgDEvn37pDL//ve/hUKhED/88IMQQog333xThIeHS/fbfOyBAwdKz++9916RmZlpcfyUlBTx2GOP2X0u3sbW/RXiahCaNGlSm9t5f+0zb9480b9/f2EymfiZ2wla3l8h+LnbEX7bNAYAVVVViImJQb9+/fDggw+itrYWAFBWVobm5makpaVJZQcNGoS4uDiUlpYCAEpLSzFkyBBERUVJZdLT02EwGHDo0CGpTMt9mMuY99HU1ISysjKLMgEBAUhLS5PK2HMu3qite2v297//HRERERg8eDBycnJw6dIlaRvvbds++eQT3HTTTZgyZQoiIyMxYsQIvPPOO9L26upq6HQ6i2vSaDRISUmx+NkNCwvDTTfdJJVJS0tDQEAA9uzZI5UZM2YMlEqlVCY9PR2VlZU4f/68VKa9fwN7zsXb2Lq/Ztu3b0dkZCQGDhyIxx9/HGfPnpW28f7a1tTUhDVr1iArKwsKhYKfuS527f014+euc/w2CKWkpCA/Px+FhYVYvXo1qqurceutt+LChQvQ6XRQKpUICwuzeE9UVBR0Oh0AQKfTWfzAmLebt7VXxmAwoKGhAWfOnIHRaLRapuU+bJ2Lt2nv3gLAAw88gDVr1mDbtm3IycnBBx98gIceekh6P+9t244dO4bVq1fj+uuvx5YtW/D444/j//2//4f33nsPwM/3x9Z1R0ZGWmzv0qULevTo4ZKf75bbbZ2Lt7F1fwFg4sSJeP/991FSUoI//vGP+Pzzz5GRkQGj0QiA99cemzZtQn19PaZPnw7Avt9Ffi7Y79r7C/BztyP8dvX5jIwM6b+HDh2KlJQU9OnTB//4xz8QEhLiwTPzfe3d2xkzZmD27NnS9iFDhiA6Ohrjx4/H999/j/79+3vilH2GyWTCTTfdhD/84Q8AgBEjRqCiogJvvfUWpk2b5uGz83323N/7779fKj9kyBAMHToU/fv3x/bt2zF+/HiPnLevyc3NRUZGBmJiYjx9Kn7J2v3l567z/LZG6FphYWG44YYbcPToUWi1WjQ1NaG+vt6izKlTp6DVagEAWq22VS9383NbZdRqNUJCQhAREYHAwECrZVruw9a5eLuW99aalJQUAJC28962LTo6GomJiRavJSQkSE2P5vO2dd2nT5+22H7lyhWcO3fOJT/fLbfbOhdvY+v+WtOvXz9ERERY/Pzy/rbt+PHjKC4uxsyZM6XX+JnrOtburzX83LWfbILQTz/9hO+//x7R0dFITk5GUFAQSkpKpO2VlZWora1FamoqACA1NRUHDx60+MArKiqCWq2WPkhTU1Mt9mEuY96HUqlEcnKyRRmTyYSSkhKpjD3n4u1a3ltrysvLAUDaznvbtptvvhmVlZUWr3333Xfo06cPAKBv377QarUW12QwGLBnzx6Ln936+nqUlZVJZbZu3QqTySR9OKampmLHjh1obm6WyhQVFWHgwIEIDw+XyrT3b2DPuXgbW/fXmv/85z84e/asxc8v72/b8vLyEBkZiczMTOk1fua6jrX7aw0/dx3g6d7anWXhwoVi+/btorq6Wnz55ZciLS1NREREiNOnTwshrg7vi4uLE1u3bhVfffWVSE1NFampqdL7zUMNJ0yYIMrLy0VhYaHo1auX1aGGixYtEkeOHBGrVq2yOtRQpVKJ/Px8cfjwYTF79mwRFhZm0XPf1rl4m/bu7dGjR8Xvfvc78dVXX4nq6mrx8ccfi379+okxY8ZI7+e9bdvevXtFly5dxAsvvCCqqqrE3//+dxEaGirWrFkjlXnxxRdFWFiY+Pjjj8WBAwfEpEmTrA6fHzFihNizZ4/YuXOnuP766y2Gd9fX14uoqCjx8MMPi4qKCrF+/XoRGhraanh3ly5dxJ/+9Cdx5MgRsXz5cqvDu22dizexdX8vXLggfvOb34jS0lJRXV0tiouLRVJSkrj++uvF5cuXpf3w/lpnNBpFXFycWLJkSatt/MztuLbuLz93O8Zvg9B9990noqOjhVKpFNddd5247777LOYKaWhoEE888YQIDw8XoaGh4te//rWoq6uz2EdNTY3IyMgQISEhIiIiQixcuFA0NzdblNm2bZsYPny4UCqVol+/fiIvL6/Vubz++usiLi5OKJVKMXLkSLF7926L7facizdp797W1taKMWPGiB49egiVSiUGDBggFi1aZDGfhRC8t+359NNPxeDBg4VKpRKDBg0Sb7/9tsV2k8kknn32WREVFSVUKpUYP368qKystChz9uxZMXXqVNGtWzehVqvFo48+Ki5cuGBR5ptvvhG33HKLUKlU4rrrrhMvvvhiq3P5xz/+IW644QahVCrFjTfeKDZv3uzwuXib9u7vpUuXxIQJE0SvXr1EUFCQ6NOnj5g1a5bFh7wQvL9t2bJliwBg9Rz5mdtxbd1ffu52jEIIITxdK0VERETkCbLpI0RERER0LQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpKt/w8rQo6OmHVIIQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 1.00769 0.11834\n",
      "amped unit avg and sd usage are 1.01973 0.10776\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "f8d72684-2b88-43f7-8509-b77bbfec8db7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final check -- any more disco's ?\n",
      "working on HD 10 time is now 0 sec\n",
      "working on HD 607 time is now 2 sec\n",
      "working on HD 1176 time is now 5 sec\n",
      "working on HD 1696 time is now 8 sec\n",
      "working on HD 2238 time is now 10 sec\n",
      "working on HD 2788 time is now 13 sec\n",
      "working on HD 3365 time is now 15 sec\n",
      "working on HD 3877 time is now 18 sec\n",
      "working on HD 4567 time is now 20 sec\n",
      "out of 4207 total HDs, there were 4207 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 22 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "6b4cba34-3451-4ffd-a2d9-31a8565a9e72",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 2179 overPopped HDs to within 1908\n",
      "working on squaring down HD 25 time is now 0\n",
      "working on squaring down HD 116 time is now 3\n",
      "working on squaring down HD 219 time is now 6\n",
      "working on squaring down HD 262 time is now 12\n",
      "working on squaring down HD 337 time is now 17\n",
      "working on squaring down HD 380 time is now 19\n",
      "working on squaring down HD 427 time is now 22\n",
      "working on squaring down HD 477 time is now 25\n",
      "working on squaring down HD 530 time is now 30\n",
      "working on squaring down HD 580 time is now 33\n",
      "working on squaring down HD 636 time is now 36\n",
      "working on squaring down HD 763 time is now 38\n",
      "working on squaring down HD 814 time is now 40\n",
      "working on squaring down HD 862 time is now 42\n",
      "working on squaring down HD 927 time is now 44\n",
      "working on squaring down HD 972 time is now 45\n",
      "working on squaring down HD 1018 time is now 47\n",
      "working on squaring down HD 1072 time is now 48\n",
      "working on squaring down HD 1130 time is now 51\n",
      "working on squaring down HD 1192 time is now 55\n",
      "working on squaring down HD 1252 time is now 58\n",
      "working on squaring down HD 1304 time is now 61\n",
      "working on squaring down HD 1348 time is now 63\n",
      "working on squaring down HD 1464 time is now 68\n",
      "working on squaring down HD 1510 time is now 72\n",
      "working on squaring down HD 1558 time is now 74\n",
      "working on squaring down HD 1602 time is now 77\n",
      "working on squaring down HD 1640 time is now 80\n",
      "working on squaring down HD 1697 time is now 82\n",
      "working on squaring down HD 1815 time is now 87\n",
      "working on squaring down HD 1859 time is now 91\n",
      "working on squaring down HD 1904 time is now 93\n",
      "working on squaring down HD 1949 time is now 97\n",
      "working on squaring down HD 1990 time is now 100\n",
      "working on squaring down HD 2032 time is now 103\n",
      "working on squaring down HD 2076 time is now 105\n",
      "working on squaring down HD 2116 time is now 108\n",
      "working on squaring down HD 2162 time is now 111\n",
      "working on squaring down HD 2200 time is now 117\n",
      "working on squaring down HD 2240 time is now 119\n",
      "working on squaring down HD 2277 time is now 123\n",
      "working on squaring down HD 2318 time is now 129\n",
      "working on squaring down HD 2359 time is now 132\n",
      "working on squaring down HD 2398 time is now 135\n",
      "working on squaring down HD 2501 time is now 140\n",
      "working on squaring down HD 2572 time is now 146\n",
      "working on squaring down HD 2675 time is now 153\n",
      "working on squaring down HD 2796 time is now 159\n",
      "working on squaring down HD 2863 time is now 162\n",
      "working on squaring down HD 2909 time is now 164\n",
      "working on squaring down HD 2977 time is now 166\n",
      "working on squaring down HD 3027 time is now 169\n",
      "working on squaring down HD 3151 time is now 181\n",
      "working on squaring down HD 3201 time is now 185\n",
      "working on squaring down HD 3296 time is now 188\n",
      "working on squaring down HD 3358 time is now 191\n",
      "working on squaring down HD 3419 time is now 193\n",
      "working on squaring down HD 3466 time is now 197\n",
      "working on squaring down HD 3540 time is now 209\n",
      "working on squaring down HD 3579 time is now 211\n",
      "working on squaring down HD 3646 time is now 218\n",
      "working on squaring down HD 3718 time is now 221\n",
      "working on squaring down HD 3777 time is now 225\n",
      "working on squaring down HD 3843 time is now 229\n",
      "working on squaring down HD 3893 time is now 232\n",
      "working on squaring down HD 4129 time is now 236\n",
      "working on squaring down HD 4175 time is now 238\n",
      "working on squaring down HD 4213 time is now 241\n",
      "working on squaring down HD 4260 time is now 243\n",
      "working on squaring down HD 4305 time is now 246\n",
      "working on squaring down HD 4450 time is now 250\n",
      "working on squaring down HD 4571 time is now 256\n",
      "working on squaring down HD 4739 time is now 258\n",
      "We have addressed overpop in a total of 2179 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%30 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "orig unit avg and sd usage are 1.00769 0.11834\n",
      "amped unit avg and sd usage are 1.01973 0.10776\n",
      "shed unit avg and sd usage are 1.00003 0.09831\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 10 out of 4805\n",
      "working on HD 398 out of 4805\n",
      "working on HD 776 out of 4805\n",
      "working on HD 1076 out of 4805\n",
      "working on HD 1377 out of 4805\n",
      "working on HD 1696 out of 4805\n",
      "working on HD 2038 out of 4805\n",
      "working on HD 2338 out of 4805\n",
      "working on HD 2654 out of 4805\n",
      "working on HD 3011 out of 4805\n",
      "working on HD 3365 out of 4805\n",
      "working on HD 3665 out of 4805\n",
      "working on HD 4031 out of 4805\n",
      "working on HD 4437 out of 4805\n",
      "working on HD 4798 out of 4805\n",
      "all done checking HD and complement contiguity for all 4805 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 899\n",
      "overpop contigFail, enclaveFail = 0 0 out of 2179\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 4143 with pop 154316.0 now has use 0.96497\n",
      "CCB unit 4144 with pop 24060.0 now has use 1.00227\n",
      "CCB unit 4145 with pop 88252.0 now has use 0.922\n",
      "CCB unit 4146 with pop 125341.0 now has use 0.95366\n",
      "CCB unit 4147 with pop 160383.0 now has use 0.92369\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "1e5b8eae-d417-437e-85d9-0c1a33c27bb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3172 763079.5151431548 0.98478\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4126 2638 0.9548227736118498\n",
      "1186 0.0 0.8683463882029316\n",
      "4139 1334 0.9401080567656152\n",
      "4143 154316.0 0.964970275678321\n",
      "1472 2389 0.8565779701253144\n",
      "3905 3608 0.8987811511729856\n",
      "3906 2298 0.949137952728858\n",
      "1479 2199 0.8671242485034415\n",
      "2760 2605 0.9916185994950121\n",
      "1480 1845 0.9213358877386443\n",
      "1483 1367 0.8690252113094854\n",
      "2383 2896 0.9217075627475736\n",
      "2767 2034 0.9712100279982274\n",
      "2395 1094 0.9455205740830162\n",
      "3173 1883 0.8794230777916928\n"
     ]
    }
   ],
   "source": [
    "# MI-only triage\n",
    "for t in popHDlist:\n",
    "    if HDvPop[t] > 1.01 * aDP or HDvPop[t] < 0.99* aDP:\n",
    "        print(t,HDvPop[t], r5(HDvPop[t]/aDP))\n",
    "        for u in HDunitList[t]:\n",
    "            plotPoly(unitGeom[u])\n",
    "            plotPoly(unitCP[u].buffer(0.002))\n",
    "            if unitCP[u].x < -86.22:\n",
    "                plotCenter(u,unitGeom[u],6)\n",
    "        candidateList, candidateUse, candidatePop = list(), list(), list()\n",
    "        for uu in getAdjoiners(HDunitList[t],unitNbrs):\n",
    "            if unitUse[uu] < 1.:\n",
    "                candidateList.append(uu)\n",
    "                candidateUse.append(unitUse[uu])\n",
    "                candidatePop.append(unitPop[uu])\n",
    "            plotPoly(unitGeom[uu],0.2)\n",
    "        plotPoly(hdCP[t].buffer(0.1))\n",
    "        plotPoly(MAP)\n",
    "        plt.show()\n",
    "        for i, uu in enumerate(candidateList):\n",
    "            print(uu, candidatePop[i], candidateUse[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "f764d533-414f-47f0-be2c-5ca5c874de4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u = 4143  #MI only triage\n",
    "plotPoly(unitGeom[u])\n",
    "for uu in unitNbrs[u]:\n",
    "    plotPoly(unitGeom[uu],0.2)\n",
    "    plotCenter(uu, unitGeom[uu])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "4acdbb55-d94b-469b-a840-5f6f6b2d8975",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3172 0.9847832833268976\n",
      "we fixed the problematic HD 3172 .  Its pop is now 773191\n"
     ]
    }
   ],
   "source": [
    "brokenT = 3172  #fixing this pesky underpopped HD with queen-adjacency problem  (MI only)\n",
    "print(brokenT, HDvPop[brokenT]/aDP)\n",
    "shedU = 4123  #this border unit is creating a false (queen) adjacency\n",
    "HDunitList[brokenT].remove(shedU)\n",
    "HDvPop[brokenT] -= unitPop[shedU]\n",
    "for t in [brokenT]:\n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    barredList = [shedU]\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist + barredList and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "    else:\n",
    "        print(\"we fixed the problematic HD\",t,\".  Its pop is now\", int(HDvPop[t]) )\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "af4a9d26-98ab-4ceb-96c1-f2083abcc5d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will remove CCB unit 1647 from HD 962 leaving it with pop 740526.0\n",
      "we will remove CCB unit 1647 from HD 1321 leaving it with pop 715813.0\n",
      "we will remove CCB unit 1648 from HD 1481 leaving it with pop 426197.66490213\n",
      "we will remove CCB unit 1647 from HD 1999 leaving it with pop 738985.0\n"
     ]
    }
   ],
   "source": [
    "#see MD code if we need to remove a stuck CCB's from any HDs and then redo the square-up after dropping them\n",
    "                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"unpatchedContigGoodPop.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv ./2024state_HD_output/MI4805unpatchedContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of HDweight should be unity, actually is 0.9999999999998046\n",
      "I will now renormalize\n",
      "normalized weight is now 0.9999999999999999\n",
      "I read in 4805 HD lists of units\n",
      "orig unit avg and sd usage are 1.00003 0.09832\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 0\n",
      "working on HD 800\n",
      "working on HD 1600\n",
      "working on HD 2400\n",
      "working on HD 3200\n",
      "working on HD 4000\n",
      "working on HD 4800\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 300\n",
      "working on HD 600\n",
      "working on HD 900\n",
      "working on HD 1200\n",
      "working on HD 1500\n",
      "working on HD 1800\n",
      "working on HD 2100\n",
      "working on HD 2400\n",
      "working on HD 2700\n",
      "working on HD 3000\n",
      "working on HD 3300\n",
      "working on HD 3600\n",
      "working on HD 3900\n",
      "working on HD 4200\n",
      "working on HD 4500\n",
      "working on HD 4800\n",
      "out of 4207 total HDs, there were 4207 0 0 0 HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "4143 0.96497 154316.0\n",
      "4144 1.00227 24060.0\n",
      "4145 0.922 88252.0\n",
      "4146 0.95366 125341.0\n",
      "4147 0.92369 160383.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will stop patching on individual underused units when their usage exceeds 0.95\n",
      "this would currently cover a total of 1172 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 600\n",
      "enter updated stopMinUse value for ending usage boost on a unit 0.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1304 units out of 4148 with usage below 0.96\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 38743\n",
      "current avg and SD of unit usage are 1.00003 0.09832 . Now trying to increase up to 600 units' underusage\n",
      "all done trying to reduce usage of unit 2469 final usage = 0.89303 67 sec elapsed 65 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00004 0.09592\n",
      "all done trying to reduce usage of unit 3395 final usage = 0.79834 185 sec elapsed 54 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00008 0.09495\n",
      "all done trying to reduce usage of unit 185 final usage = 0.92051 221 sec elapsed 59 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00009 0.09246\n",
      "all done trying to reduce usage of unit 102 final usage = 0.96032 349 sec elapsed 92 3 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.0001 0.08965\n",
      "all done trying to reduce usage of unit 169 final usage = 0.96112 460 sec elapsed 82 2 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.0001 0.08757\n",
      "all done trying to reduce usage of unit 124 final usage = 0.96029 622 sec elapsed 97 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00015 0.08561\n",
      "all done trying to reduce usage of unit 3574 final usage = 0.96734 710 sec elapsed 105 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00022 0.08377\n",
      "all done trying to reduce usage of unit 160 final usage = 0.96127 842 sec elapsed 82 3 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00022 0.08244\n",
      "all done trying to reduce usage of unit 3439 final usage = 0.78141 875 sec elapsed 10 1 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.00022 0.08229\n",
      "starting to increase usage for unit 3440 with usage 0.75707 . Total UU units tried = 10\n",
      "all done trying to reduce usage of unit 3440 final usage = 0.78141 912 sec elapsed 10 1 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.00022 0.08214\n",
      "all done trying to reduce usage of unit 3442 final usage = 0.84062 999 sec elapsed 31 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00023 0.08167\n",
      "all done trying to reduce usage of unit 138 final usage = 0.96669 1163 sec elapsed 83 2 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00024 0.07951\n",
      "all done trying to reduce usage of unit 199 final usage = 0.96357 1312 sec elapsed 79 2 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00025 0.07852\n",
      "all done trying to reduce usage of unit 2444 final usage = 0.96018 1416 sec elapsed 85 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00025 0.07737\n",
      "all done trying to reduce usage of unit 1436 final usage = 0.96182 1529 sec elapsed 56 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00028 0.07553\n",
      "all done trying to reduce usage of unit 207 final usage = 0.96037 1716 sec elapsed 75 2 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00028 0.07452\n",
      "all done trying to reduce usage of unit 213 final usage = 0.96061 1922 sec elapsed 76 5 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00028 0.07365\n",
      "all done trying to reduce usage of unit 3894 final usage = 0.96038 2172 sec elapsed 84 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00028 0.07281\n",
      "all done trying to reduce usage of unit 3035 final usage = 0.96246 2301 sec elapsed 61 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00029 0.07107\n",
      "starting to increase usage for unit 2095 with usage 0.79691 . Total UU units tried = 20\n",
      "all done trying to reduce usage of unit 2095 final usage = 0.89183 2457 sec elapsed 45 3 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.00029 0.0706\n",
      "all done trying to reduce usage of unit 1447 final usage = 0.96161 2574 sec elapsed 60 0 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.0003 0.06913\n",
      "all done trying to reduce usage of unit 54 final usage = 0.89852 2713 sec elapsed 38 0 successful, failed patches, couldn't start= 113\n",
      "current avg and SD of usage are 1.0003 0.06859\n",
      "all done trying to reduce usage of unit 43 final usage = 0.96135 2853 sec elapsed 55 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.0003 0.06746\n",
      "all done trying to reduce usage of unit 3586 final usage = 0.96267 3074 sec elapsed 91 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00031 0.06661\n",
      "all done trying to reduce usage of unit 55 final usage = 0.96131 3277 sec elapsed 55 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00031 0.06566\n",
      "all done trying to reduce usage of unit 1466 final usage = 0.88709 3438 sec elapsed 40 0 successful, failed patches, couldn't start= 105\n",
      "current avg and SD of usage are 1.00031 0.06518\n",
      "all done trying to reduce usage of unit 1427 final usage = 0.96045 3670 sec elapsed 73 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00031 0.06454\n",
      "all done trying to reduce usage of unit 3393 final usage = 0.93659 3906 sec elapsed 61 2 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.0003 0.06406\n",
      "all done trying to reduce usage of unit 42 final usage = 0.96081 4101 sec elapsed 49 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.0003 0.06334\n",
      "starting to increase usage for unit 41 with usage 0.80807 . Total UU units tried = 30\n",
      "all done trying to reduce usage of unit 41 final usage = 0.96094 4303 sec elapsed 49 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.0003 0.0628\n",
      "all done trying to reduce usage of unit 3992 final usage = 0.9601 4393 sec elapsed 57 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00032 0.06204\n",
      "all done trying to reduce usage of unit 2097 final usage = 0.95857 4798 sec elapsed 76 2 successful, failed patches, couldn't start= 55\n",
      "current avg and SD of usage are 1.00032 0.06167\n",
      "all done trying to reduce usage of unit 3991 final usage = 0.96143 4877 sec elapsed 57 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00032 0.06112\n",
      "all done trying to reduce usage of unit 2098 final usage = 0.9556 5336 sec elapsed 75 4 successful, failed patches, couldn't start= 55\n",
      "current avg and SD of usage are 1.00032 0.06083\n",
      "all done trying to reduce usage of unit 3392 final usage = 0.96006 5637 sec elapsed 60 3 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00032 0.06061\n",
      "all done trying to reduce usage of unit 44 final usage = 0.96169 5778 sec elapsed 45 0 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00033 0.05987\n",
      "all done trying to reduce usage of unit 3441 final usage = 0.93839 6021 sec elapsed 45 0 successful, failed patches, couldn't start= 56\n",
      "current avg and SD of usage are 1.00033 0.05964\n",
      "all done trying to reduce usage of unit 3412 final usage = 0.96428 6357 sec elapsed 60 1 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00033 0.05935\n",
      "all done trying to reduce usage of unit 3580 final usage = 0.96206 6551 sec elapsed 59 0 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.00033 0.05896\n",
      "starting to increase usage for unit 3067 with usage 0.83823 . Total UU units tried = 40\n",
      "all done trying to reduce usage of unit 3067 final usage = 0.96034 6650 sec elapsed 40 4 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00033 0.05807\n",
      "all done trying to reduce usage of unit 1449 final usage = 0.96091 6771 sec elapsed 39 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00033 0.05752\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[263], line 136\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m excess \u001b[38;5;241m-\u001b[39m unitPop[shedU] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39mmaxGap:\n\u001b[1;32m--> 136\u001b[0m     contig,cContig, __, ___ \u001b[38;5;241m=\u001b[39m \u001b[43menclaveCheck\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrySet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdifference\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[43mshedU\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munitNbrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    137\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m contig \u001b[38;5;129;01mand\u001b[39;00m cContig:\n\u001b[0;32m    138\u001b[0m         notYetPicked \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[2], line 727\u001b[0m, in \u001b[0;36menclaveCheck\u001b[1;34m(UNITLIST, UNITNBRS, maxLoops)\u001b[0m\n\u001b[0;32m    725\u001b[0m BDRYLIST \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m (\u001b[38;5;28mset\u001b[39m(get2nbrs(ADJLIST,UNITNBRS))\u001b[38;5;241m.\u001b[39mintersection(UNITSET) )  \u001b[38;5;66;03m#in case inHD boundary is only queen-adjacent\u001b[39;00m\n\u001b[0;32m    726\u001b[0m noEnclave, enclaveList \u001b[38;5;241m=\u001b[39m   isContiguous(ADJLIST, UNITNBRS)  \n\u001b[1;32m--> 727\u001b[0m unbroken, smallPieceList \u001b[38;5;241m=\u001b[39m \u001b[43misContiguous\u001b[49m\u001b[43m(\u001b[49m\u001b[43mBDRYLIST\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mUNITNBRS\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    728\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unbroken: \u001b[38;5;66;03m#could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\u001b[39;00m\n\u001b[0;32m    729\u001b[0m     unbroken, smallPieceList \u001b[38;5;241m=\u001b[39m isContiguous(UNITLIST, UNITNBRS)  \u001b[38;5;66;03m#slower check for full district, not just its boundary \u001b[39;00m\n",
      "Cell \u001b[1;32mIn[2], line -1\u001b[0m, in \u001b[0;36misContiguous\u001b[1;34m(VLIST, NEIGHBORLIST, returnBiggestPiece)\u001b[0m\n\u001b[0;32m      0\u001b[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overpopped first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"starting to increase usage for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Total UU units tried =\",len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to reduce usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00003 1.00033 and their SDs are 0.09832 0.05746\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 300 out of 4805\n",
      "working on HD 600 out of 4805\n",
      "working on HD 900 out of 4805\n",
      "working on HD 1200 out of 4805\n",
      "working on HD 1500 out of 4805\n",
      "working on HD 1800 out of 4805\n",
      "working on HD 2100 out of 4805\n",
      "working on HD 2400 out of 4805\n",
      "working on HD 2700 out of 4805\n",
      "working on HD 3000 out of 4805\n",
      "working on HD 3300 out of 4805\n",
      "working on HD 3600 out of 4805\n",
      "working on HD 3900 out of 4805\n",
      "working on HD 4200 out of 4805\n",
      "working on HD 4500 out of 4805\n",
      "working on HD 4800 out of 4805\n",
      "all done checking HD and complement contiguity for all 4805 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.08\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 686 units out of 4148 with usage above 1.05\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 23246\n",
      "current avg and SD of unit usage are 1.00033 0.05746 . Now trying to reduce up to 686 units' overusage\n",
      "starting to reduce usage for unit 3308 with usage 1.21926 . Total units tried = 1\n",
      "try to drop unit 3308 from 27 th HD.  usage down to 1.1552110965639029\n",
      "try to drop unit 3308 from 54 th HD.  usage down to 1.0770006084887955\n",
      "all done trying to reduce usage of unit 3308 final usage = 1.04981 26 sec elapsed 53 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00037 0.05602\n",
      "starting to reduce usage for unit 3622 with usage 1.33523 . Total units tried = 2\n",
      "try to drop unit 3622 from 38 th HD.  usage down to 1.258066993129816\n",
      "try to drop unit 3622 from 76 th HD.  usage down to 1.188239841367037\n",
      "try to drop unit 3622 from 114 th HD.  usage down to 1.1072611487984165\n",
      "all done trying to reduce usage of unit 3622 final usage = 1.04855 66 sec elapsed 95 0 successful, failed patches, couldn't start= 47\n",
      "current avg and SD of usage are 1.00035 0.05461\n",
      "starting to reduce usage for unit 3317 with usage 1.15501 . Total units tried = 3\n",
      "try to drop unit 3317 from 21 th HD.  usage down to 1.098556468467974\n",
      "all done trying to reduce usage of unit 3317 final usage = 1.04862 74 sec elapsed 31 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00033 0.05398\n",
      "starting to reduce usage for unit 3266 with usage 1.15058 . Total units tried = 4\n",
      "try to drop unit 3266 from 19 th HD.  usage down to 1.1091917939836833\n",
      "try to drop unit 3266 from 38 th HD.  usage down to 1.0766211902505423\n",
      "all done trying to reduce usage of unit 3266 final usage = 1.04718 78 sec elapsed 32 0 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.00033 0.05327\n",
      "starting to reduce usage for unit 3552 with usage 1.13736 . Total units tried = 5\n",
      "try to drop unit 3552 from 23 th HD.  usage down to 1.0811212970425705\n",
      "all done trying to reduce usage of unit 3552 final usage = 1.04902 82 sec elapsed 24 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00033 0.05269\n",
      "starting to reduce usage for unit 3321 with usage 1.12538 . Total units tried = 6\n",
      "try to drop unit 3321 from 19 th HD.  usage down to 1.0823873150416927\n",
      "all done trying to reduce usage of unit 3321 final usage = 1.0485 96 sec elapsed 23 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00032 0.05215\n",
      "starting to reduce usage for unit 4035 with usage 1.12205 . Total units tried = 7\n",
      "all done trying to reduce usage of unit 4035 final usage = 1.04841 100 sec elapsed 25 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00032 0.05169\n",
      "starting to reduce usage for unit 3219 with usage 1.12522 . Total units tried = 8\n",
      "try to drop unit 3219 from 26 th HD.  usage down to 1.069956850854416\n",
      "all done trying to reduce usage of unit 3219 final usage = 1.04803 107 sec elapsed 34 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00031 0.05124\n",
      "starting to reduce usage for unit 2814 with usage 1.12345 . Total units tried = 9\n",
      "try to drop unit 2814 from 17 th HD.  usage down to 1.099204318657135\n",
      "try to drop unit 2814 from 34 th HD.  usage down to 1.0951688056262474\n",
      "try to drop unit 2814 from 51 th HD.  usage down to 1.0951688056262474\n",
      "try to drop unit 2814 from 68 th HD.  usage down to 1.0951688056262474\n",
      "try to drop unit 2814 from 85 th HD.  usage down to 1.0951688056262474\n",
      "all done trying to reduce usage of unit 2814 final usage = 1.09517 115 sec elapsed 7 0 successful, failed patches, couldn't start= 80\n",
      "current avg and SD of usage are 1.00031 0.05107\n",
      "starting to reduce usage for unit 3296 with usage 1.11758 . Total units tried = 10\n",
      "try to drop unit 3296 from 15 th HD.  usage down to 1.0958411760416897\n",
      "try to drop unit 3296 from 30 th HD.  usage down to 1.0759565630644214\n",
      "all done trying to reduce usage of unit 3296 final usage = 1.04754 121 sec elapsed 25 8 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.0003 0.05062\n",
      "starting to reduce usage for unit 781 with usage 1.11555 . Total units tried = 11\n",
      "all done trying to reduce usage of unit 781 final usage = 1.04523 123 sec elapsed 20 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00029 0.05026\n",
      "starting to reduce usage for unit 3046 with usage 1.11164 . Total units tried = 12\n",
      "all done trying to reduce usage of unit 3046 final usage = 1.04882 130 sec elapsed 17 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00028 0.04988\n",
      "starting to reduce usage for unit 3309 with usage 1.11974 . Total units tried = 13\n",
      "try to drop unit 3309 from 23 th HD.  usage down to 1.0621516756671112\n",
      "all done trying to reduce usage of unit 3309 final usage = 1.04681 154 sec elapsed 21 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00028 0.0494\n",
      "starting to reduce usage for unit 3281 with usage 1.10771 . Total units tried = 14\n",
      "try to drop unit 3281 from 14 th HD.  usage down to 1.073767810234134\n",
      "all done trying to reduce usage of unit 3281 final usage = 1.04595 157 sec elapsed 17 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00027 0.04908\n",
      "starting to reduce usage for unit 3216 with usage 1.10598 . Total units tried = 15\n",
      "try to drop unit 3216 from 18 th HD.  usage down to 1.0509201214912849\n",
      "all done trying to reduce usage of unit 3216 final usage = 1.04596 160 sec elapsed 18 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00027 0.04879\n",
      "starting to reduce usage for unit 1850 with usage 1.11084 . Total units tried = 16\n",
      "try to drop unit 1850 from 25 th HD.  usage down to 1.0535837923885465\n",
      "all done trying to reduce usage of unit 1850 final usage = 1.04952 176 sec elapsed 22 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00026 0.04851\n",
      "starting to reduce usage for unit 1299 with usage 1.10539 . Total units tried = 17\n",
      "try to drop unit 1299 from 28 th HD.  usage down to 1.0737484521607619\n",
      "all done trying to reduce usage of unit 1299 final usage = 1.04807 182 sec elapsed 20 8 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00026 0.04816\n",
      "starting to reduce usage for unit 1285 with usage 1.10108 . Total units tried = 18\n",
      "all done trying to reduce usage of unit 1285 final usage = 1.04814 187 sec elapsed 15 2 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00026 0.0478\n",
      "starting to reduce usage for unit 2803 with usage 1.1054 . Total units tried = 19\n",
      "try to drop unit 2803 from 13 th HD.  usage down to 1.0775645736932968\n",
      "all done trying to reduce usage of unit 2803 final usage = 1.04776 196 sec elapsed 14 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00025 0.04748\n",
      "starting to reduce usage for unit 3294 with usage 1.10525 . Total units tried = 20\n",
      "try to drop unit 3294 from 12 th HD.  usage down to 1.0899511595808709\n",
      "try to drop unit 3294 from 24 th HD.  usage down to 1.0679036045378034\n",
      "all done trying to reduce usage of unit 3294 final usage = 1.04989 200 sec elapsed 18 3 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00025 0.0472\n",
      "starting to reduce usage for unit 51 with usage 1.10069 . Total units tried = 21\n",
      "try to drop unit 51 from 30 th HD.  usage down to 1.0523823013006595\n",
      "all done trying to reduce usage of unit 51 final usage = 1.049 208 sec elapsed 20 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00024 0.04691\n",
      "starting to reduce usage for unit 3306 with usage 1.09543 . Total units tried = 22\n",
      "try to drop unit 3306 from 25 th HD.  usage down to 1.092601925095892\n",
      "try to drop unit 3306 from 50 th HD.  usage down to 1.072152056375891\n",
      "try to drop unit 3306 from 75 th HD.  usage down to 1.0552782690785298\n",
      "all done trying to reduce usage of unit 3306 final usage = 1.04761 211 sec elapsed 13 0 successful, failed patches, couldn't start= 67\n",
      "current avg and SD of usage are 1.00024 0.04655\n",
      "starting to reduce usage for unit 787 with usage 1.09277 . Total units tried = 23\n",
      "all done trying to reduce usage of unit 787 final usage = 1.04848 215 sec elapsed 12 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00024 0.04634\n",
      "starting to reduce usage for unit 3361 with usage 1.09168 . Total units tried = 24\n",
      "all done trying to reduce usage of unit 3361 final usage = 1.04982 223 sec elapsed 15 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00025 0.04614\n",
      "starting to reduce usage for unit 2339 with usage 1.09703 . Total units tried = 25\n",
      "try to drop unit 2339 from 19 th HD.  usage down to 1.0702368976493652\n",
      "all done trying to reduce usage of unit 2339 final usage = 1.04982 232 sec elapsed 15 9 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00025 0.04581\n",
      "starting to reduce usage for unit 3834 with usage 1.09133 . Total units tried = 26\n",
      "all done trying to reduce usage of unit 3834 final usage = 1.04687 235 sec elapsed 14 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00024 0.04559\n",
      "starting to reduce usage for unit 3477 with usage 1.0881 . Total units tried = 27\n",
      "all done trying to reduce usage of unit 3477 final usage = 1.04977 238 sec elapsed 17 6 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00023 0.04542\n",
      "starting to reduce usage for unit 1675 with usage 1.08713 . Total units tried = 28\n",
      "all done trying to reduce usage of unit 1675 final usage = 1.04516 241 sec elapsed 13 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00022 0.04526\n",
      "starting to reduce usage for unit 3072 with usage 1.08461 . Total units tried = 29\n",
      "try to drop unit 3072 from 24 th HD.  usage down to 1.0525694293433787\n",
      "all done trying to reduce usage of unit 3072 final usage = 1.04859 245 sec elapsed 15 5 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00021 0.04511\n",
      "starting to reduce usage for unit 2651 with usage 1.10028 . Total units tried = 30\n",
      "all done trying to reduce usage of unit 2651 final usage = 1.04897 248 sec elapsed 17 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0002 0.04488\n",
      "starting to reduce usage for unit 607 with usage 1.08197 . Total units tried = 31\n",
      "try to drop unit 607 from 22 th HD.  usage down to 1.0645830496837791\n",
      "try to drop unit 607 from 44 th HD.  usage down to 1.0624265603091034\n",
      "try to drop unit 607 from 66 th HD.  usage down to 1.0552201948583613\n",
      "try to drop unit 607 from 88 th HD.  usage down to 1.052521679429009\n",
      "all done trying to reduce usage of unit 607 final usage = 1.04926 253 sec elapsed 10 0 successful, failed patches, couldn't start= 95\n",
      "current avg and SD of usage are 1.0002 0.04471\n",
      "starting to reduce usage for unit 4021 with usage 1.08118 . Total units tried = 32\n",
      "all done trying to reduce usage of unit 4021 final usage = 1.04722 255 sec elapsed 9 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00019 0.04455\n",
      "starting to reduce usage for unit 2348 with usage 1.08049 . Total units tried = 33\n",
      "all done trying to reduce usage of unit 2348 final usage = 1.04576 268 sec elapsed 10 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00019 0.04433\n",
      "starting to reduce usage for unit 1776 with usage 1.08053 . Total units tried = 34\n",
      "all done trying to reduce usage of unit 1776 final usage = 1.04968 275 sec elapsed 18 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00018 0.04416\n",
      "starting to reduce usage for unit 3362 with usage 1.0779 . Total units tried = 35\n",
      "all done trying to reduce usage of unit 3362 final usage = 1.04902 277 sec elapsed 9 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00018 0.04403\n",
      "starting to reduce usage for unit 3275 with usage 1.07704 . Total units tried = 36\n",
      "all done trying to reduce usage of unit 3275 final usage = 1.04562 280 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00018 0.0439\n",
      "starting to reduce usage for unit 1284 with usage 1.0794 . Total units tried = 37\n",
      "try to drop unit 1284 from 16 th HD.  usage down to 1.0613657378878527\n",
      "all done trying to reduce usage of unit 1284 final usage = 1.04772 288 sec elapsed 9 3 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00018 0.0437\n",
      "starting to reduce usage for unit 3337 with usage 1.0812 . Total units tried = 38\n",
      "all done trying to reduce usage of unit 3337 final usage = 1.04911 291 sec elapsed 10 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00018 0.04344\n",
      "starting to reduce usage for unit 4080 with usage 1.07533 . Total units tried = 39\n",
      "try to drop unit 4080 from 18 th HD.  usage down to 1.0731302843507353\n",
      "try to drop unit 4080 from 36 th HD.  usage down to 1.07095959905558\n",
      "try to drop unit 4080 from 54 th HD.  usage down to 1.0538935215626317\n",
      "try to drop unit 4080 from 72 th HD.  usage down to 1.051884153545653\n",
      "all done trying to reduce usage of unit 4080 final usage = 1.04931 295 sec elapsed 10 0 successful, failed patches, couldn't start= 62\n",
      "current avg and SD of usage are 1.00018 0.04328\n",
      "starting to reduce usage for unit 2991 with usage 1.07437 . Total units tried = 40\n",
      "all done trying to reduce usage of unit 2991 final usage = 1.04833 297 sec elapsed 6 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00018 0.04317\n",
      "starting to reduce usage for unit 238 with usage 1.07421 . Total units tried = 41\n",
      "all done trying to reduce usage of unit 238 final usage = 1.04957 301 sec elapsed 12 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00018 0.04305\n",
      "starting to reduce usage for unit 3268 with usage 1.07692 . Total units tried = 42\n",
      "try to drop unit 3268 from 11 th HD.  usage down to 1.0583484595168124\n",
      "all done trying to reduce usage of unit 3268 final usage = 1.04566 303 sec elapsed 8 2 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00017 0.04292\n",
      "starting to reduce usage for unit 3016 with usage 1.07276 . Total units tried = 43\n",
      "try to drop unit 3016 from 10 th HD.  usage down to 1.0630111741252324\n",
      "all done trying to reduce usage of unit 3016 final usage = 1.04996 306 sec elapsed 8 3 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00017 0.04284\n",
      "starting to reduce usage for unit 2937 with usage 1.07287 . Total units tried = 44\n",
      "all done trying to reduce usage of unit 2937 final usage = 1.04805 320 sec elapsed 6 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00017 0.04269\n",
      "starting to reduce usage for unit 3270 with usage 1.07055 . Total units tried = 45\n",
      "try to drop unit 3270 from 19 th HD.  usage down to 1.0551685733293599\n",
      "all done trying to reduce usage of unit 3270 final usage = 1.04895 322 sec elapsed 9 10 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00016 0.0426\n",
      "starting to reduce usage for unit 3149 with usage 1.0697 . Total units tried = 46\n",
      "all done trying to reduce usage of unit 3149 final usage = 1.04836 328 sec elapsed 8 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00017 0.04248\n",
      "starting to reduce usage for unit 3956 with usage 1.06908 . Total units tried = 47\n",
      "all done trying to reduce usage of unit 3956 final usage = 1.04802 337 sec elapsed 7 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00017 0.04233\n",
      "starting to reduce usage for unit 3322 with usage 1.07218 . Total units tried = 48\n",
      "all done trying to reduce usage of unit 3322 final usage = 1.04727 353 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00017 0.0422\n",
      "starting to reduce usage for unit 3551 with usage 1.06822 . Total units tried = 49\n",
      "try to drop unit 3551 from 21 th HD.  usage down to 1.0620652096059724\n",
      "all done trying to reduce usage of unit 3551 final usage = 1.04938 357 sec elapsed 11 13 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00016 0.04212\n",
      "starting to reduce usage for unit 3482 with usage 1.06715 . Total units tried = 50\n",
      "all done trying to reduce usage of unit 3482 final usage = 1.0467 360 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00016 0.04204\n",
      "starting to reduce usage for unit 2945 with usage 1.06666 . Total units tried = 51\n",
      "all done trying to reduce usage of unit 2945 final usage = 1.04771 362 sec elapsed 7 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00016 0.04191\n",
      "starting to reduce usage for unit 1225 with usage 1.06653 . Total units tried = 52\n",
      "all done trying to reduce usage of unit 1225 final usage = 1.04936 366 sec elapsed 5 1 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00016 0.04179\n",
      "starting to reduce usage for unit 3560 with usage 1.0665 . Total units tried = 53\n",
      "try to drop unit 3560 from 17 th HD.  usage down to 1.0533127793612007\n",
      "all done trying to reduce usage of unit 3560 final usage = 1.04985 369 sec elapsed 7 13 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00016 0.04174\n",
      "starting to reduce usage for unit 3385 with usage 1.06642 . Total units tried = 54\n",
      "all done trying to reduce usage of unit 3385 final usage = 1.04615 375 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00016 0.04165\n",
      "starting to reduce usage for unit 2988 with usage 1.06584 . Total units tried = 55\n",
      "try to drop unit 2988 from 12 th HD.  usage down to 1.0630524713484493\n",
      "all done trying to reduce usage of unit 2988 final usage = 1.0494 377 sec elapsed 4 12 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00015 0.04158\n",
      "starting to reduce usage for unit 4043 with usage 1.06532 . Total units tried = 56\n",
      "all done trying to reduce usage of unit 4043 final usage = 1.04615 379 sec elapsed 5 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00015 0.0415\n",
      "starting to reduce usage for unit 3469 with usage 1.0647 . Total units tried = 57\n",
      "all done trying to reduce usage of unit 3469 final usage = 1.04807 380 sec elapsed 4 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00015 0.04144\n",
      "starting to reduce usage for unit 4032 with usage 1.07269 . Total units tried = 58\n",
      "try to drop unit 4032 from 14 th HD.  usage down to 1.0640681249318404\n",
      "try to drop unit 4032 from 28 th HD.  usage down to 1.0640681249318404\n",
      "try to drop unit 4032 from 42 th HD.  usage down to 1.0640681249318404\n",
      "try to drop unit 4032 from 56 th HD.  usage down to 1.0640681249318404\n",
      "try to drop unit 4032 from 70 th HD.  usage down to 1.0640681249318404\n",
      "all done trying to reduce usage of unit 4032 final usage = 1.06407 383 sec elapsed 2 0 successful, failed patches, couldn't start= 69\n",
      "current avg and SD of usage are 1.00015 0.04141\n",
      "starting to reduce usage for unit 3369 with usage 1.0642 . Total units tried = 59\n",
      "try to drop unit 3369 from 10 th HD.  usage down to 1.055732538533866\n",
      "all done trying to reduce usage of unit 3369 final usage = 1.04728 386 sec elapsed 6 6 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00015 0.04133\n",
      "starting to reduce usage for unit 4070 with usage 1.06348 . Total units tried = 60\n",
      "all done trying to reduce usage of unit 4070 final usage = 1.04778 388 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00015 0.04128\n",
      "starting to reduce usage for unit 2763 with usage 1.06327 . Total units tried = 61\n",
      "all done trying to reduce usage of unit 2763 final usage = 1.0483 391 sec elapsed 6 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00014 0.04122\n",
      "starting to reduce usage for unit 3232 with usage 1.06445 . Total units tried = 62\n",
      "all done trying to reduce usage of unit 3232 final usage = 1.04787 394 sec elapsed 4 1 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00014 0.04115\n",
      "starting to reduce usage for unit 4046 with usage 1.06317 . Total units tried = 63\n",
      "try to drop unit 4046 from 13 th HD.  usage down to 1.063169910326949\n",
      "try to drop unit 4046 from 26 th HD.  usage down to 1.063169910326949\n",
      "try to drop unit 4046 from 39 th HD.  usage down to 1.063169910326949\n",
      "try to drop unit 4046 from 52 th HD.  usage down to 1.063169910326949\n",
      "try to drop unit 4046 from 65 th HD.  usage down to 1.0598080582497311\n",
      "all done trying to reduce usage of unit 4046 final usage = 1.05981 396 sec elapsed 1 0 successful, failed patches, couldn't start= 66\n",
      "current avg and SD of usage are 1.00014 0.04113\n",
      "starting to reduce usage for unit 4033 with usage 1.07178 . Total units tried = 64\n",
      "try to drop unit 4033 from 14 th HD.  usage down to 1.0717816719051703\n",
      "try to drop unit 4033 from 28 th HD.  usage down to 1.068803109680903\n",
      "try to drop unit 4033 from 42 th HD.  usage down to 1.068803109680903\n",
      "try to drop unit 4033 from 56 th HD.  usage down to 1.0616302982240209\n",
      "try to drop unit 4033 from 70 th HD.  usage down to 1.0595576938339966\n",
      "all done trying to reduce usage of unit 4033 final usage = 1.05956 399 sec elapsed 4 0 successful, failed patches, couldn't start= 68\n",
      "current avg and SD of usage are 1.00014 0.04106\n",
      "starting to reduce usage for unit 3015 with usage 1.06257 . Total units tried = 65\n",
      "all done trying to reduce usage of unit 3015 final usage = 1.04854 400 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00014 0.04101\n",
      "starting to reduce usage for unit 2819 with usage 1.06292 . Total units tried = 66\n",
      "all done trying to reduce usage of unit 2819 final usage = 1.04839 402 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00014 0.04092\n",
      "starting to reduce usage for unit 4041 with usage 1.06236 . Total units tried = 67\n",
      "all done trying to reduce usage of unit 4041 final usage = 1.04856 404 sec elapsed 4 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00014 0.04087\n",
      "starting to reduce usage for unit 2466 with usage 1.06238 . Total units tried = 68\n",
      "try to drop unit 2466 from 21 th HD.  usage down to 1.0538186703455716\n",
      "all done trying to reduce usage of unit 2466 final usage = 1.04735 406 sec elapsed 5 0 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.00013 0.04082\n",
      "starting to reduce usage for unit 2993 with usage 1.06076 . Total units tried = 69\n",
      "try to drop unit 2993 from 23 th HD.  usage down to 1.0504452034243232\n",
      "all done trying to reduce usage of unit 2993 final usage = 1.04665 409 sec elapsed 7 13 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00013 0.04077\n",
      "starting to reduce usage for unit 3923 with usage 1.06066 . Total units tried = 70\n",
      "all done trying to reduce usage of unit 3923 final usage = 1.04885 414 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00013 0.04071\n",
      "starting to reduce usage for unit 1254 with usage 1.06007 . Total units tried = 71\n",
      "all done trying to reduce usage of unit 1254 final usage = 1.04959 419 sec elapsed 3 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00013 0.04066\n",
      "starting to reduce usage for unit 3358 with usage 1.05989 . Total units tried = 72\n",
      "all done trying to reduce usage of unit 3358 final usage = 1.04692 420 sec elapsed 3 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00013 0.04061\n",
      "starting to reduce usage for unit 309 with usage 1.05958 . Total units tried = 73\n",
      "all done trying to reduce usage of unit 309 final usage = 1.04866 423 sec elapsed 4 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00013 0.04055\n",
      "starting to reduce usage for unit 3342 with usage 1.05877 . Total units tried = 74\n",
      "all done trying to reduce usage of unit 3342 final usage = 1.0498 425 sec elapsed 3 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00013 0.04047\n",
      "starting to reduce usage for unit 1330 with usage 1.05998 . Total units tried = 75\n",
      "all done trying to reduce usage of unit 1330 final usage = 1.04894 428 sec elapsed 4 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00013 0.04041\n",
      "starting to reduce usage for unit 1653 with usage 1.05778 . Total units tried = 76\n",
      "all done trying to reduce usage of unit 1653 final usage = 1.04648 431 sec elapsed 6 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00013 0.04036\n",
      "starting to reduce usage for unit 3355 with usage 1.05761 . Total units tried = 77\n",
      "try to drop unit 3355 from 9 th HD.  usage down to 1.0563119901970746\n",
      "all done trying to reduce usage of unit 3355 final usage = 1.04965 437 sec elapsed 3 7 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00013 0.04033\n",
      "starting to reduce usage for unit 272 with usage 1.05749 . Total units tried = 78\n",
      "try to drop unit 272 from 12 th HD.  usage down to 1.0505058587208675\n",
      "all done trying to reduce usage of unit 272 final usage = 1.04789 440 sec elapsed 5 6 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00012 0.0403\n",
      "starting to reduce usage for unit 2338 with usage 1.05744 . Total units tried = 79\n",
      "all done trying to reduce usage of unit 2338 final usage = 1.04837 444 sec elapsed 4 3 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00012 0.04024\n",
      "starting to reduce usage for unit 3316 with usage 1.05704 . Total units tried = 80\n",
      "all done trying to reduce usage of unit 3316 final usage = 1.0498 446 sec elapsed 2 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00012 0.04021\n",
      "starting to reduce usage for unit 985 with usage 1.05628 . Total units tried = 81\n",
      "all done trying to reduce usage of unit 985 final usage = 1.04465 448 sec elapsed 2 2 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00013 0.04014\n",
      "starting to reduce usage for unit 600 with usage 1.05602 . Total units tried = 82\n",
      "all done trying to reduce usage of unit 600 final usage = 1.04841 449 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00012 0.04011\n",
      "starting to reduce usage for unit 3018 with usage 1.05546 . Total units tried = 83\n",
      "try to drop unit 3018 from 9 th HD.  usage down to 1.0554589444300806\n",
      "try to drop unit 3018 from 18 th HD.  usage down to 1.050709763760536\n",
      "all done trying to reduce usage of unit 3018 final usage = 1.047 451 sec elapsed 2 0 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00012 0.04009\n",
      "starting to reduce usage for unit 1632 with usage 1.05538 . Total units tried = 84\n",
      "all done trying to reduce usage of unit 1632 final usage = 1.04956 453 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00012 0.04006\n",
      "starting to reduce usage for unit 3256 with usage 1.05496 . Total units tried = 85\n",
      "all done trying to reduce usage of unit 3256 final usage = 1.04838 455 sec elapsed 2 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00012 0.04003\n",
      "starting to reduce usage for unit 1274 with usage 1.05479 . Total units tried = 86\n",
      "all done trying to reduce usage of unit 1274 final usage = 1.04862 459 sec elapsed 2 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00012 0.04\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run first and third for MA\n",
    "maxSD = 0.04 #0.06  #0.08\n",
    "stopMaxUse = 1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "90492bd1-3320-4712-a1be-0fc321c682bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.05\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will stop patching on individual underused units when their usage exceeds 0.97\n",
      "this would currently cover a total of 228 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 150\n",
      "enter updated stopMinUse value for ending usage boost on a unit 0.97\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 228 units out of 1650 with usage below 0.97\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.03\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 23164\n",
      "current avg and SD of unit usage are 0.99949 0.04454 . Now trying to increase up to 150 units' underusage\n",
      "all done trying to reduce usage of unit 699 final usage = 0.97145 5 sec elapsed 35 32 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.9995 0.04374\n",
      "all done trying to reduce usage of unit 775 final usage = 0.84636 7 sec elapsed 2 21 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.9995 0.04363\n",
      "all done trying to reduce usage of unit 778 final usage = 0.93854 14 sec elapsed 27 19 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.99951 0.04306\n",
      "all done trying to reduce usage of unit 726 final usage = 0.89843 22 sec elapsed 28 100 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 0.99952 0.04238\n",
      "all done trying to reduce usage of unit 727 final usage = 0.9712 30 sec elapsed 38 39 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99953 0.0417\n",
      "all done trying to reduce usage of unit 1611 final usage = 0.97236 36 sec elapsed 23 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99953 0.04131\n",
      "all done trying to reduce usage of unit 618 final usage = 0.8655 38 sec elapsed 5 18 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99954 0.04108\n",
      "all done trying to reduce usage of unit 617 final usage = 0.88308 41 sec elapsed 9 6 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99953 0.04079\n",
      "all done trying to reduce usage of unit 866 final usage = 0.88738 46 sec elapsed 9 11 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99954 0.04057\n",
      "starting to increase usage for unit 1606 with usage 0.86625 . Total UU units tried = 10\n",
      "all done trying to reduce usage of unit 1606 final usage = 0.97485 50 sec elapsed 26 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99955 0.03991\n",
      "all done trying to reduce usage of unit 1279 final usage = 0.97492 56 sec elapsed 30 1 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99958 0.03911\n",
      "all done trying to reduce usage of unit 745 final usage = 0.97346 62 sec elapsed 26 27 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99958 0.03859\n",
      "all done trying to reduce usage of unit 1638 final usage = 0.97341 66 sec elapsed 17 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99959 0.0383\n",
      "all done trying to reduce usage of unit 1636 final usage = 0.97484 72 sec elapsed 18 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99959 0.03815\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[178], line 147\u001b[0m\n\u001b[0;32m    145\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m excess \u001b[38;5;241m-\u001b[39m unitPop[u] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39mmaxGap \u001b[38;5;129;01mand\u001b[39;00m u \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m shedCandidates:\n\u001b[0;32m    146\u001b[0m         shedCandidates\u001b[38;5;241m.\u001b[39mappend(u)  \u001b[38;5;66;03m#we'll check enclavity if ever picked\u001b[39;00m\n\u001b[1;32m--> 147\u001b[0m         shedScores\u001b[38;5;241m.\u001b[39mappend((unitUse[u] \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1.\u001b[39m) \u001b[38;5;241m*\u001b[39m unitCP[u]\u001b[38;5;241m.\u001b[39mdistance(hdCP[t])\u001b[38;5;241m/\u001b[39mavgDist[t])\n\u001b[0;32m    148\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m kkk, u \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(shedCandidates): \u001b[38;5;66;03m#checking if this shed eliminates high-pop future sheds ...\u001b[39;00m\n\u001b[0;32m    149\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m excess \u001b[38;5;241m-\u001b[39m unitPop[u] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39mmaxGap:\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\shapely\\geometry\\base.py:334\u001b[0m, in \u001b[0;36mBaseGeometry.distance\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    332\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdistance\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m    333\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Unitless distance to other geometry (float)\"\"\"\u001b[39;00m\n\u001b[1;32m--> 334\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _maybe_unpack(shapely\u001b[38;5;241m.\u001b[39mdistance(\u001b[38;5;28mself\u001b[39m, other))\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\shapely\\decorators.py:77\u001b[0m, in \u001b[0;36mmultithreading_enabled.<locals>.wrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m array_args:\n\u001b[0;32m     76\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m---> 77\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     79\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m arr, old_flag \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(array_args, old_flags):\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\shapely\\measurement.py:72\u001b[0m, in \u001b[0;36mdistance\u001b[1;34m(a, b, **kwargs)\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[38;5;129m@multithreading_enabled\u001b[39m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdistance\u001b[39m(a, b, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m     49\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Computes the Cartesian distance between two geometries.\u001b[39;00m\n\u001b[0;32m     50\u001b[0m \n\u001b[0;32m     51\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[38;5;124;03m    nan\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mdistance(a, b, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#OPTIONAL ADDITIONAL ROUND OF UNDERPOPPED.  USED FOR MD, NOT NEEDED FOR MI\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.05  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"starting to increase usage for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Total UU units tried =\",len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to reduce usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.06\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.94\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for MA, above is second run-through after overuse, then underuse pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00003 1.00012 and their SDs are 0.09832 0.04\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 300 out of 4805\n",
      "working on HD 600 out of 4805\n",
      "working on HD 900 out of 4805\n",
      "working on HD 1200 out of 4805\n",
      "working on HD 1500 out of 4805\n",
      "working on HD 1800 out of 4805\n",
      "working on HD 2100 out of 4805\n",
      "working on HD 2400 out of 4805\n",
      "working on HD 2700 out of 4805\n",
      "working on HD 3000 out of 4805\n",
      "working on HD 3300 out of 4805\n",
      "working on HD 3600 out of 4805\n",
      "working on HD 3900 out of 4805\n",
      "working on HD 4200 out of 4805\n",
      "working on HD 4500 out of 4805\n",
      "working on HD 4800 out of 4805\n",
      "all done checking HD and complement contiguity for all 4805 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    }
   ],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in range(nVTDs):\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "working on full or partial VTD master list for unit 4000\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7211 7213 7211 7211 7211 7211\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999999 4805\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "12 1 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "\n",
    "plt.scatter([v for v in range(nVTDs)], vtdUSE)\n",
    "plt.show()\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for MI\n",
      "In year/election 2020 Dem and GOP total votes were 2804034 2649859 for a stateGOP of 0.48587\n",
      "There are a total of 4805  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 2268840 2279544 for a stateGOP of 0.50118\n",
      "There are a total of 4805  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for MI\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv MI4805patchedWithCutsVTDs.csv\n",
      "enter the number of HD-drawn districts in the state; e.g. 8 13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 4806 drawn districts.  This state has 13 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of HD-drawn districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedDF['GEOID20'], \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c38b293-7a46-4106-a972-b4868206bc2d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Rep-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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dYQEBXDT/Mph/GUyaRPyUCRDuSUG2fpTX7e4T40eQGD+C2roK9u/+HGQ9cktlWkGzPUJRXIwdfy3y9yGQtxcE6HVcHB6EKgS76xrZ5WogUK9jcrA/ugvRvaehoeEV7c54BnC56nC5aggKGn22RTlFJGL8wolJuQpFVdlWvI29pdsJt0QyLnoK8pny6XewbDTMmUfG6s001JQxa+Z0DI89TMQLz7W4VsZUH4E6kMoq0VcUYP3oVQD0o5ORfrkI17UeN5ha2/Nut8FBEYyb9AOf6zMy1+BwNmK5kIN3TwG5KUUXPE3u1jY1uRvsbybJohXG09C40NGUjzNAVdVGoqKuPNti9AuqqiLLMjpZ5qK46QDk1xexKvcrZElm0oAZBJtDTrsca/MPUr/0CLOsDrbtLyEoq4H4qCjyVuwmKmU4hT//LSl12RjXroVhAQTc+oRnw18+Q0DzJDf9EoDT8ajToaByepWxCyVcNkivY15T/ZAjDTY+LK7kiohgQjSXjIbGBYv26z7NVFdvIyR0ygWRMSLrzChKI7Ic0G55QmAsCYHX4FLcbC3ahN1VR0xAAqOjxvXbvmudjXySu53g7bv5wWefMWPFt+gbGpGee47LJ8XDpKZg2NxcWL+E2IuGU54XSdw7b0LqDf0mR1ucDisVNaVIkg6DTo+sM6LT69HrjKhIKKr7tOz3Qmaov4XBfmZ21jZQ61bw18lMCQ5ArwWoamhcUGjKx2nE4SgFSYfJGHG2RekX9Do/FKUegyHA63qDTs+shNkAZNXk8N2JZeglPVPi0vE39q0x2dHaYr7M342/3syPUi4iuMwAsbFYDx+k4tnfM+i11+CJJ9pto7jc5K3JIDg5ChYtwrDya7jrsW73lXHgWxS3A2iK3hDgHxxFfPwoSkuzqK082S47VzZYCAqJQQiVRsWForgQbieq4kJyO/E3ez9PGl0jS1JLSfd6t8L66nq21lh5YGC01mVXQ+MCQVM+ThNCCGpqdvW5ady5iE7nh6JYezQ2LSSZtJBkbG4H2wo34FJsDAwZypCelHXPzUVMmkRxchwxldX86kQeUnAwuFywcCHgycapnTABXn8dKitbgkZJSqLovqdJSIomMDkGLh6LK8zdI9eKxS8Ua10JY8df27KsvLKArOObCYtKY9TYq89cXIsGAIF6HXPDg5gVGsjG6noaFE/dkKnB/poioqFxHqMpH6eJyqr1hIenn96dnOFy4TpdAC5Xfa+2sehNXDzQE9B5qDyT704sw6QLYFrcTIx6g9dtbC4nhYPjSTqajz4mBiQJFi/2KBgzZoAQBIeGM0FVISICHn4Y/v3vlu1VRcGtuLHVWnG73ajunrk/jOYAAuS4dssiw+OJDD8365t8nxwRBllq6SvjVgXbaq00KipxZiMjAixnWToNDY3eoikfp4HGxhyMxgj0+jNgdj+D5cL1hkAUpXfKR1uGR45keORI6h11bMz/DkVVGG2LIWbW5WC1gsmEGDCA7/7vEuYHRqGfnQrXXAN33glPPQW/+Q3IMsyejcNgYF30SIaU5BG6eTebVmS37Kc0F9Jqj+Fn0aPXyziNgUzxIZNQVbJz92CtLkSSZcaMu7rPx6dxZtDLUksl1QK7k+8qatFJElM0a4iGxnmDpnz0M0IoWK1HiYo6wz1DzkC5cL0+mAZH2SnPE2gKYm7yfFRVJWPnF+hGJaELCEb+f29gv/IyLv9oLfriEo+isXw5uN2wZw9cfz088ghkZCDJEpX3Xkby/90LfnquuizV5/4OrPUuc2HREYpP7iY+9SLSUiae8nFpnHnizUbizUbcquCjg8WMsctER/sTkRBwQQR5a2hcqGjKRz9TWbWR8PBZZ26HXZQL728M+iDcrrp+m0+WZca4ImFrJs4AE9WzJxFVZMVtMmDXScgqGOxOAKxxMVgHxmFZ+imO8GCCD2dx+W25cPw43HJL1ztq8xBqtFaRfWwjblUhMDSeidO62fYc5kJJte0P9LJEcKPCkIkxOOpc5GZUIgED0oIx+Xl372loaJw9NOWjH3E4ytHrAtDp/M7cTpvdLh9+CGvXwqWXnrZdGQwhuHsYcNoJb/EpQ4eizr8CyeXCUO0iutrzQP3kR9cwctMeRhaXYZs0GPOuTFwTBxN8LA9TfiGVj/4IwwffsufFv3NJVjYcONDlrlXFReaBFbgcVnSWEIaMvhKTj3gTjfMXVQWDTsYQaiIg1ISqCkqya2mocWAONBA3OBRZS9nV0Dgn0EL3+5Ga2l2EhJwl8/3ChbBqlSfz4zQhy2aE6ur7BOnpsG4deW//lrpH7mZb9n9oTBkAEtgDA1F0Hn/9VZ+vZnDOSeR6K4bMY8iqIGzZRvxOFKObPIXU9zYQFBSHn39dt8f9ydESDhjjSEqbyrhJNzB65FxN8bhAsZh05Fc2tHyWZYnYQSEMmhRNdFIQx3eWknOgAmu14yxKqaGhAZrlo9+oqdlFcHD/FdXqE4sWwZtvwmPd17ToCx4feu+N/aqqUntsL5aqcup2rqd4ZwaJpkimpt4Opm8RSEiqik5VEYDFZkNGRpVUhE4PoX6emJarr4YTJ+CTT5AeehBFVTw78HLcpQ0O3tibx6XJkUwbMrNl+b7ck2RX1XY6irP9PtxWnp7KYrc2QlLM6RDnvMRk1KPXeX+fMpr1DJkSgxCCinwrFfn16IwysWkh6PTaO5iGxplGUz76AZerGlV1YDad4QdBUpLH5dLMPfecgZ327jFdm3UYW2khodGJmDIOYX7wCcL37IGlSwFQMvYhIzDabNRMHkfw9j2UjhqFxeEg5NBBrKOHEuYXBllZEBgIDgdMmoQ0fDgjf/AT9t93B1UzJuBqzKd+zTYk2UC1y8XeukbSk8IpKs3j09LWhnE5+Xk8fO1V/XlCzhqf7una3fR9w+1WMRm6znaRJInIxEAiEwNxORUKj1WjugVBERbCYvtWCE9DQ6P3aMpHP1BVvZWoyCvOthhniJ5ZPtyOBsp3bMUvKY6Yi+Z5Yj7S08l++Q8Y//cJCWvXwuDB2IbEE5CZh6QohGzfA0BkeRmG4hIkRSVo/yGE1YY0YoSnlserr8KaNchLlhBWUEDYggVwzzPs2fM2l06d2q1cG6w1KIqCTqelZF5ouNwqhl5YMQxGHYnDPcXpassbyT1QARIMSAvBZNFujRoapxPtF3aKVFdvIzRkspbW14b6omM0ZOcTPW02sr7pEisogC+/JOz4UbA5sTY0sj04juluA4SFeTJ2tm6F0hLMLjfqC39AeeoJCAulMTYCeUoMUpo/ujf+iBodivXA/0N3vJiAxlJqD7yCX21wj2QzGo3Y7Xb8/bW33AsNt1vF1EcXSnCkH8GRfk1BqjU4bQrmAAPRyUHab1tD4zSgOTtPAYejDJAxXiC9W04FR3UlJVtWUbpjHUqjk5iZc1sVj2YiIzFt3cqyBb9E1LtI+uh9JFcN9oFRNJblYL9sLkKSUM16rOXfotMZURcvwR0dg+VALea3l2OQQjDtySf8nk8Ivu01im/7ERXlkVQUlfdITqPRiNPpPA1n4Myjpdq2R0FgOMVsFk+QaihJoyMIjrRwMrOSXC1IVUOj39EsH33E07tl5wXVu6VntL+5q6qbgmMZFFfVE502laQo71VdRVwcTJ1CRvZezPZ8agOM/G9iCtGp85n/1UqiMg+i5JeComIfO5WacbcQlPJbTEuX4pRkWHSrJ6j0Bz+Ajz+GTZso+s09xFcK1IuuY5ChJ91bWi0fGhceEvSrlcISaCRpVIQWpKqhcRrQlI8+UlW1ibCwGWdbjLNGvaOWrwv2Y5QkLNXhpIXHsyunskX5EEIghGhpxLZswydcsXo1I7KzmHD0GO/+8nKMk0exnwSO3/EbFrzzIp/f8Wt+dWA90W/8jYTyEtDrqRmSQP0PnyFwYlPF2Nxc2L8fZ0MNtbOnEffE39D9+tcQ3jM3iiRJiNNcCVbjwqJTkOrRalRFC1LV0DgVNOWjDzQ0ZKPX+2Mw9CzO4ELDrbr5IGcHdw6ei16WIcWzfP+uPI6X1JFZWEuNzYmfxUB141aGmEKICIrEOHcexiVLOPjco9zZKKMseBS3IiD3JKblkSRMHob/sFjk997C9vbfKdq1mrhn/0bIoWMt+y47sh1LWT5le9cwZM7NOG6rxdTL9OILx4evKVHtOAOnw2DUkTiifZCqJEvEpAZrQaoaGr3glGyHL774IpIk8cADDwCQm5uLJEle/z755JP+kPesI4RKXd0+goPHn21Rzhqf5mzilpTpHsWjDdOSwhFOhUtGRXHlhEgmDA4gMHYAF0++jIvGXQx4an4Uz5mJWLUK8grRO53oVTdCUYlJGUBpTiHOqDAa//wiycNnY/YLaplfuBzUmCFww3ZSZ1yPrNPz8hXXtyoeubkQGQmzZ3uqqX777Rk6IxoXDL24hoIj/UgaHUHC8DAqC+rJPVBBSU7tGRNVQ+N8ps+q+s6dO/nnP//J6NGjW5YlJCRQXFzcbty//vUvXnrpJa644sJIRV2ft5YhYZPPthhnDavbjkmvI8DY2dwcF+EP+PPKweVEW0Jxqm6ujGtfeG3j52+T/pNfo5r9Ydw4Gn/xCO6xE+GWn8PXG5EO70EuLiU8IwemT2/XryZ/31riRrW6uiRJ4onU2PZC+OjyK4QgLy+PQ4cOMW3atP47IWeRC8V+02/01wnpZafo5iBVgPoqO9s+38CAtCDih41E1zHoWkNDA+ij8mG1Wrnlllt48803+f3vf9+yXKfTERPTvtDW559/zo033khAwBloL3+ayWl0EBwyhcN1R9lcnotJ1nFx7BivD+ILEZu1gr17C/i/Hzzgdf3inB1U2WsptVXxwIgr261bWrUP16M30bg/gzEjxqH7/V8wGp0E3ncvPNfqMgmekIb1g7cIPHoUKTERbrvNU9/j8sux1ZbjHx7XI1mVykqcNhvb161rWTZgwACGDBmC/gJ5IGhOl9NMHzpFB4aZkWSF2MFDyc/cj6IoRA5MJigi8vTIqKFxntKnu/C9997L/PnzmTdvXjvloyO7d+9m3759/P3vf/c5xuFw4HC0prHV1fVf19T+xK0KDlptXBUVAsETALC5bawt3I9NcRFjCeKiAWPOrpD9iLW2hLrqXGISJ1NTfpyik+uRkAiKvhZZ9lKgKzeXaybMw2/0+KbGcRHt3hidipMbJ90IkZOoeHMtR3NzSTXaMXW4ucuyTGDKENxVRfDh+xh+uAjx2GMclUuIGT7dp7x2u53jGRkMWbUK26hRBGRnY33zTdLT09vFeGRkZFxAMR8XynGcY5xip2ghBAaTmaSxnvtEWe4Jyk+ewGTxJ3boMO+/Hw2N7xm9Vj4WL17Mnj172LlzZ7dj3377bYYNG8b06b4fGi+88AK/+93veivGGWdlZS2XRAS1W2bRW7hyoKeq5tLczWdDrFPG4aggL/9tZNmILOkBiYqCXIIDJ+EXOIDDu/5NQHACwybcjk5nJGvjRp9z5Y4ezOFnb8dSVsXU+3/Gd/95ocXsXG+rbxkXnn2YkNf+gJyZCV980WkeSa/HkDyM2g3LMV08C/m//yX5ohsw+Qe2jLHZbGRkZNDY2Oi52RsMjIiPxzjPE9TKhx8SeeAAdFA0hBAXkPKhcVo4xU7RHa+vqKQUopJSsDdYyd2/B6GqRA5M0awhGt9reqV85Ofnc//997Ny5UrMZnOXY202Gx988AFPPfVUl+Mef/xxHnrooZbPdXV1JCQk9Eas085hq40UPzMm2Xd87vn4OCsu/oKGhmOkpvwKWW69FKpPvMXgsTcBEJ86q9021qJ8Fu/cDbSa/SXAv6iIsXIwN8y6HTIzsevfZkbMZOKTUzvtV5o9G/urr2J77TUiO97ck5LgH/9ATJqEbtdOjGVl8NnnNAqJvdu2tdToMBqNjBgxguDgNhlHubkeudxOClIDifjN+xj/8Xd0Y5utMc+hxsQgyzKqQ6F+VyHBFyWewhnUOJfodzfUwoXwl7/Aww9DePgpT2f2DyBl3CSg2RqSg95o1GJDNL6X9OqK3717N2VlZYwf35rpoSgKGzZs4LXXXsPhcLT0zFiyZAmNjY3ceuutXc5pMpkwmXpWIOpsYFNU8uxOLou4ANJqc3M9EfwjRuCsycf82G0MuPm3nYapbhVrSTnoZCSdjDkoAF1TG/pUg46pkyZ4mTscMg7AjBmIffvIf/Z3+HWhkkmSROXIkYS+8AJ119yELjICoXoeH1J+IWJgCnuefZroLVsIeP99ioKDGTlyJIGBgT7ndJQcw1GWR8nmpYQPHE7tReOJ3pcN69a1Bg+++SYAskmHLcJB7up83EVbmXDjlWDyEpfU5pw1KzDdBSCeObSoj9POaeoU3WwNcdnt5GXuR1UUwmLjCB3Qs5gmDY3znV4pH3PnziUjI6PdskWLFjF06FAeffTRds263n77ba655hoiI89v0+KqyjqujLwAFI8mxKxZ5Lw0i5CGWMLu+BN4UT7CA8KozS1GuAVCUWmoq2Ho1Rd3P3mTufr4bx4naO9eIn55n8+h5ogIAuITqJ93CRUvvIr/rx/0eEgkCUNtI6pD4eLbbvM0lNu5k4HXXgveslSEoHjPt9TVVmOKSCJh7VYM27+iJv8oKc++gvTrX4MQNGZlYHY7UVW1pfBZzJBBxAwB6MbS1svsB42zQ79YH/ulU3TPJTGYzSQ3xYZUFuSTvXs7Op2euOEjMRjP3ZcyDY1TpVfKR2BgICNHjmy3zN/fn/Dw8HbLs7Ky2LBhA8uXL+8fKc8Se+saGR1oQdfWh3tOvwn7xuEoozT/P4Q1HGfAgFexZFf5jOQ3B/oxICio5TjtRSVgeKXHx1k+O51BTzyFVFfX2VzddHPXAfEzZ8DMGRxdmcHgGa3XT91+FdXPjNT80P/DH+Chh+DGG9tNVXZkG1X5x4gcNp0hE9JaltfXVOLnH0Dujm9IWPUdrtHDMGfnUfGnJ6g9soGdpcWY/TpbOSRJQlEUoqKiGDFiROcD60P2w+nkfHT1fT/o2zUSHp9AeHwCbpeLgsOZKC4nQZHRRCYm9a94GhrnAKfF0fjOO+8QHx/Ppb0M1DqXqHMrVLvcjAvy67zyPHkTVhQbhYUf4nbXodcHEhd7M7rdf4dLbuxZJH/TcRa9+wkpTz3V4+MUQsDtt/fZXL2nqJGZH7wHj/7as+Daa+Hzz1vW19jtLN25h1GN+5hwWec309FX3uH5R24uzFuLvil4MOrAAZzTJxOePApLaEyn7ZrJzs5m9erV6HQ6TMXFTFi1CveECViOHkXqQ/bD6eLcUYM02nKqSqHeYCBptKc+Tm1ZCVm7thMeF6+5ZDQuKE5Z+VjXpo5CM88//zzPP//8qU59VllbVcc1kSFdDzrH3oTB0+itrOxrGhqykXUmYgf8EJMpyrMyN7dPkfyy1QpC4Kyrw91g9W39aTJXO5xOpHvv7fMxSBLodHJLyqO6bx9fP/U0ySWl1DXYOFlVxU2TxnD8aC/SspuCB+VLJnY7NDU1ldTUpkDZ3FzE3LmU/PWvFP/1r6R8/DHFMTHtiutpaLTFYbP221zBUTEER8WQvXu7pnxoXFBoIdZe2FpjZWpwgO+UzFOsA9AtXbh2nG47FbYKwi2RmPQmhFCoqz9EVdUmhOpCkiTCw2cTE3Ntl7uoGJtC0O+fpuqKMYjg9kGcTkcVBLUeZ8Lu3WTdfhfu5V9iKmiqYNuF9ceomtiyek+7OatzjxAa27ofU2kpo+//FfYBAxntUii989c4Js4GoCCvmm9ryhg1fDTbf/cn6j/9lDFHczheUk5iRDgLJ43H5XL2/rwuWoTfZ8th3EW92kySJBITE4l96SXUiRM5lJUFTcpHUVERAwYM0NJ3NVowWXwHRfcVvcGEy+nQ4kA0Lhg05aMD5U4XqhBEmwy+B51iHQCAOmcjR6tPUGcrBuFus0ZgyC9l0vSJqJ8s4ej+5aTeeR87h/wVCRlZNhBkDudY1RFU1Y1DKJhcBcxKvQ293ouLyAcuVyXGex8gZkuud9dIGyuJ7sMPiVv8OZabbyHr3X+3H+fF+hNYWs3Yu37cTnna6B/I9Cuubj3KnBwaR0+k9ncvY7W6iX/kTtwzxhJ7wzyuiUslUNggMJDrZ0/gq4QE4q65hLEJz7XEkEiSBELtOgbHS/BgyaYvSe5jkSe9Xg8//SlDN25kudmMv78/paWlDB8+vFMslMaZ59yyQfYv8cNHkJ95oKVwmYbG+Y6mfLRBCMGWGivXRoX2bIO2dQB6QFZdOScqtgES/gYLqcEpREWmI8vGduNUcqhR/kdmyQ5GyQkEG4OYlzzf65yVdisZlUd6pngkJcHLL0NkJGFJAwCT52HdHQsXYnjyaaisxN6osO+7bYxYuQr78FH45WRx/NFnqf1iXctwW2lpJ8uI6cmX2k3ptNuQkIifM47tr3/FkAAjB/aVEDn1IjLv/TPTgstg/nze2JdHYqCFyHvvbhdD4rE0ND1uehODYzDzxdZD6M2dA06dTic3X9oho8aLAhMNNBePF0KwceNG1q9fjxCCmTNntsv60jhzXMi2J53egKK4ux+ooXGeoCkfbVhfXU96aC9Nps11AG6e6XNIg9PK+pMriPWP4pLkK5Gkrh9OsiQRtn0/s370VLeuHZfqRi93f9vdteYrzH4BGIpLiBk1nIyfPMCMmZN6FDArFAXHrHno33wTv/jRpMwYDt/Nw9Bk/Rl64ABcO7t1gzFJsH+T598+4mKqMk8SlbkHZsxg4u69SF98zoiYRKr/14g1s4L8VCeWAQmMiwxkSlxop5RHSZIQQm0/aQ9icBplPy6bMozg0LBO6z5bt7vLbb0hSRKzZnkKsdXW1rJixQoGDx5MWlpaN1tqaLSiuBUqqutQEUSFBqPTd75HhMclUlmQR3i8VhhP4/xHUz6ayLM5CNLpCDF0c0p81AGoO76Gr05u7TQ8u7QcpfgEd8z7CSH+vWhA141r52RtNqWNVZxotDOsi8JbzdjrFcKjgxgYmYActoSY8PEUrTpCeL2Tkk2FGMx6YidGez3OggeeIeLhR2BEMsq/lpC3fAthBWVUfLqGoFFTCPNWBbJDXIxiba8oKA0OdHPnwJIlHLnvJSL+uwzrrYtIOLabMR/8kpAjB9ny/95idpx3K5SERENRJkfLakldswrH+JGYj2ST/9ff0pDxHdCUddOC59/FhXlsdYVj9GvApJe5NcDV4raZXVLGtp/cS8WUi7Doa4kJquq4OQDO+hAUR3RLXRJFCI4HSTglgSEhmc8OHuPXmvLxvSW7ykruN1s8l4wQnUr8Ny9GqCB5as7odBKhgR7r5f+Ob+f62WNJGTig3TYhMQPI3r1dUz40Lgg05QNQhGB/vY2ro0L6PMePUtMRHbzOX2YcYrAlgNix4zhZVETIoEG9n9hHief1ZYXMjh1LcpiecEPXpe4BJl92KVkZu7A6FYLWrye1+BbYvw/X+x8RPSqc3PWFxBLtdVu/KVOwjEgGIG3RtYjjx5G+C8N06WTyPltLmLcqkB2UJ3nqZe3mdFvtLf9OefF+9NOnUbRvDpWjJ8PXS7F98hGzjx0AFnqVSZJlxs29D+nkSaT02ciLP0D56GPiMjJx395qhWofCCqROkTgQg8I3tiXDwFyi6zBJ/OYdMP12O/9CZszv2JQ2mUd9wrAoe1bGDk5zaPcCMgurWeQSzB5iKeg3geNjb6/CI3Tx1kM+qgoOUlZ4QGQQMTBjZf67mfVFfsOnSAq1L+T4tGM3mDC5bBjMHX/m9fQOJfRlA9gdWUdc8ODuh/YBbo2QYx7jmexp6KS9IGJDIodwMGCIpRTcUh7ebgHGXQk+nuX2WFz4HLaURUHqqoiVBVVKMgSOGyNkJ6O1KQYGHdsggXz0Zt8u4KkNjEMksGANHw4LF2KoXldV1Ugm5Qn/fAp7WXMr8dR24AJsPgZ4ad3Mu7kdir1MuEBJlh0q8ci0UVfDYvZH0x+oNNjMfnBrbfDpEkYrI1d9uKwNP3frGvfq0dX70nd9TcbMRj0GI3eLVV6gx6zpTVOx+jnxGltzb4ZEhfLuqNZzB7StfVDqAKpBy4zjR5yhk9lbWUpBTnbAEFQWBxDx81HlmWyd63u9VyqqrL4my3ERYZw6zWzfI6LHz6CvMwDLVVRNTTOV3x3SvuecLzBTpzZiJ/u1E/F7tyT/HvLduxGE3dNm8Kg2Ka3l97WAml2eeTmQmQkfPwxfPopfPtty5B299nmcbNnw6RJHP/jM5TmH6GypICaimLqqstprKslICSM0KjY1u0WLoRVq6Cy0us8bfd3SixaROTK9tVuxegUih54unXBPffAz3/eabvmXiy92Vevt2l2EU2dCn2oT6OqAl0bJWJC/AAUIXht3XdUVlX53K6ioP/qQVyw9OKaVIVyWkVRVZXCE3s5uGspB3ctpariKMPGz2fExOtISJnUUra/W7wc0/++3sy8qSNJn9x11pROb0DVAk81LgC+15YPp6pyvNHOld0VE2vGR1qnw+Vg3aEtKDnZLLruLi8bCgwFhTBteu/Lsvc0k6PNuJRL5+H3zAu+j6EtzQ/r8QsB0T/VW72luMato9lTba+ox5Ffy9Bb5nba7vgf/0l4m+36sq+e05Qx09vU6Q66ZKNboeNjb+7QQUxMSmTJjt0E+Ptx04SxnaaJTOz/ehDnM6qqYnU3EGTscF6avh9b7kmk66/l4NgIhNqIIgS1jjp0kgzImEqPcmhPGgaDifCYFILDEtDpjF731VOsdeUUZO9AUVyARFTsKEZMvO6U5mx7TBQUYLtyPolv/Y+o8JAebRo5MJmy3BNEJaWcuhwaGmeJ77XysaqyjkvCe9k0rummoWQdwblgAZtiLUhC4eKh0ygszPa+jRAIJK8P9q8PlmB1eH+T8SsqZmyNjS17CnDu2svcOhtLdp0EQG9v4MuKNQBYikoYXl3G7l1rCMzKYaxLxlvi7ZZ9e6ioqMHwy/th/UbPwpGjAFAbsynfl09qVSkHd6zAPzuXodZadu9YQXF9BlP/6916EzRqSA9PXOv25ohAgkbEUrrvONFj+xAH44XHdr5HQkDnkunVVis1VbEkhg9oI4OEQOBSBG1tSJ/Gl3LJH5aw/4qR6MIierzvlHB/Vu8upri0AXdZA37Jre6w+JAk3CeOg2Yl90m9s4Gt+auRZR1mfQCNrvomhULCXFDKoIYSMnK+Ifj4SUaoTmID4tHr/JEliVCjX4vFIYcTJCdfhb2xgYqiHMryVyNQaP8t+8IzqnmsaFpmMoWQOmJeL4t79cL/U1ODzeZg5oRhPd4kKCKKstwcTfnQOK/53iofmfWNDPE3Y+iBz121lpNbdJTi7AIG1ZaTcWAdgTmFjDWYuWR0eo/21y7usU1KqNXh5qbx8d43CnPD/h3cdN/NuPfspeiN97hv4sCmlQNbx+XmwoH7iX3gt7BvH9ue+DtTvUzncDq5eNoUAn1l3eTmQsbDRD30XEuWysWTL+Vzt0rK9Ct6dJw9RW804qruP7fDwMAB/HzoJZ2WZ5bkUxhUy2VDBnrZinaWoLDgVILueZCZW/LhsVt6vO8QfyM3zPLMn7Uil7TRse3WVwTpOLJxP0NmjMZaUYvRz4zJXwsYLG0oY3/pdnSSnosHXoFB76Wwn5QLux5h7o//0HJNWrwomW0x+/kTnzYS6IfCb7m5EBff/40k22SDZTz+HOm9rA1j9vPHbrViDuhcr0ZD43zgexnz0aioFDlcpPp5fwBY68rZcHAjqw+sY/WBdawrykOKGc30oVOI2neIufc8yeRb7sb44h/bbee7wnbT+1RfYgvS02HTJk48/TKB2zZ3O4433yRiX4eU3yYf89if34PfzFldx3K0mYe1a32P684X32F98J4dLatURaE+p5Twcal9m9sLqo+4GlUIdF354r25bfrQDK8ZxdbZihWRFE3kwBgOrt1LwaFcjmzc3+f52+I+D0t6qqrKjqKtfHdiGTk1WcxLms/cZB+KRzM9vSZPF+npsG6dp7nhU0/135xNxxS1f0/34zsQN3Q4J/bu7JBOrqFx/vC9VD7WNGW3CCE4XnCMhpoi9pw8zHf717L6wDoyS/OYMmgSc0fPZu7o2cwZPIHkoCBP2mYXN0Jf9wHRnOt/CjfR2vnXY9m0rjU41BcLFxK+ezPffryr/fL0dPa98ToN//ugZzfQjsGo3ujuptxmfdwHrWXZZZ2OsInJVGXm9n3uHqKoKtIZTIPQWbwbE8MToxk5ZzzD0sciyRKH1u3jwMqdKK6+B0k6FbX7Qf1BPwQiV9qq+O7El6zK/Yr4oCQuTbmGqXHTex6kCT27Jk8np6OR5MKFRO/b1etjkmSZ1AlTOLJlAw4ttVvjPOR753bZV9fICH8TWw9vpd5pZ1hkHAcqy0gL8Gf8mIt7PlHb+hv19TBpEjEDwuDpf7aaZpsCVFMHDaKxqIjKoCA2bdwI8fHMevZZtl50EUWNEvhyu7RBlmVqbvgRMT1oU2+9YiKDPn6NvND/A0BXUkJoaRmNh48hp6V2ewO1Oxr5aMPbjJkxDP1vfkbe2DEczW/vqtGXFROVX0H+B99A1QGSrXVkb1vXst5QXEJMVTn529Zhys4h2mbl8H/fa1kvKo2EBIZRXVbbbl5LnZNdopCkklKOLv8Ov9xcBtXUsn95U+EwvHvUg0xl4MVtrtI+E6UtB3M3UFZX03I+LG1Sa3cf30qdrbzpU5tS7kg4q9sY9NsEITeWFCFddxdl4Z2dXkKoBCSm4B+dyOhLJwOw9bX/8c2GrzGHBjHykkuIGTXKq5y+8NOfwXeHPgQi2xwOtuVvwCU78DMEMjfpynYp6X3CW02Z081pbiSZc8l8wvpwTCY/P4ZOn8WJPTsIHRBPWGw/dL3tGFT/i1947nH97XbS0BDnGLW1tQIQtbW1/T53ZX21eHz7FvHtvtWipr6y9xPk5Ahx+eVCREQIkZ4uRGKiELff7ll+ww3ixNdvCpGfL8TEia3jb7jB8+8tW4QIDW2d6+9/F+KFF8S2pWt7tOu9h8tF5rGKHo3dt3ZJZ7kjIkTd2EFC9fcTYsWKLrcvriwQa3Z/2fL54Pvvdh6UkyPUiAjhGDlYqBZz5zmb9ikuukgIf39R8ewveiS7t227k1cIIbZ99g+vy3fmZ4t12Ye9rluzb6nP+dbsX+Zz3bJvs9rL2vQdH/zsvdbvvgNuh02U7d0k9uZWim0788X6dSfE5m15Ys1Hn4kj6zeK4gMHfO7PF5/s3tf1gObzmJ7ukeubb3o8d3ljlcgsPySOVWULd3ZW63WckSHEhAk+t2t0ucTK7XvEF2s2iBXrtois1X/r8T5PlRMnXuv/Sdv+hj/4QIjHHut2k2U7V/dqF4uXb+6DYO0pOn5E5B/MOOV52h1vfr4Qo0a1/+zj+tbQEKJ3z+/vleXDZavhqYBKLMOv8iyoOgGHvoDJd4Oxh43Z/vEPz5tA27fAZgS+TbOBgZDSJjq9KSVUatOQrStkWUI9FSt7ejpHnrySsftVDN2kkyqqgl7StbwFDYyIgP/3aru3HmvxQdQQHXXP/ZR4W6z3FNU2KazGz/7Ta3k7pr8e27WK2pI87zK7nF6Xq6KL3APfQTpd4msrQ6Pbt1VJAuESGPPzqT5SxB8Cg4mQJe7KP4ScY8Ww4PZ+kcPpUhBCYDI2/bR7abFQVZWPczYQZQpgQEAMjW47Kwv2MGftaoxe3v6PZZ3gSH5hi0XDgGDayKEENhV5K1i/r9fHdc7io9rwqaIoKi6322OhEx63ZG8ZkDaEuooyjm3bRNrkacinamGCzveyjp+76iitodEN3yvlIzoyCSQ3bH0dQhJBdcP422DxQrj1i95P2PbHuH49Aw7vh5PF7U2z/WSy1csSzh7GB0i+6kyrCurNN8OMOV3eQF2qC53c+vDKu+4ahs2e0+7hZQlLwRYYSGDaRBiZ3vVNeeFCTE8/5vFr9/am3eaGX3XyGBOuvsPrMFnn/VK2u1QcPiMze6l8NN1spw1IgSdVz8126NCW7zhlz25Y6v06EkDmtm2MufSHBOllfrRmKwlOAxe//ByOmDBMi5fDC3/s9c1bVVV2Hq9A1suU1TtQVIHFIONWBcaCYi4WwhPY1cN4hTVFe5geNYTEwNby3mMS9RSPGkLMwaNIiYlw223w73+TM2wEuSXlXHOx76aKKo5eHY9P+uNB1x9znAa3z1WzxvH5yh1IkowAymvq+dkP5nhtLtcVQRFR+AWFcHTrJpJGj8MS6KUCck/OQdt71uuvw69+1fK57JV3sWVW4nYqDNA7kCdNp+Kv/yHOz4p0/fWa8qHRY75XygcAEWmev7ZM/ilkrYJj38Elz4KrEfQm8FJeW3XZkNavg4umwf4MxMf/A5cNaeYMiu68gpS6wPZWgG4KWKmqisutNtUC8SBU0RJiIPD82+VWqLO7qbe72m3f/DxpeYkXAlV4f6ja3DbqXXakH9+C9MLz6N99D3X4MKSGBpxPP4Vr7sUIoL6xxmP5aNmJ2unhpapu9AEhBIxsSjXu5qbcOG8axr7etJvmloeEYjD2LkXVqIPC2nqyKsrRyRJ6WUYnScgy1Nnqqa8vAwRCqKiq2vRvBbez3vuE6els/L/nuW6iH+5rryPrj/8iIGk01aPSiYhPwP+l56lasR1kFRGtkPzwsy2bBg4LwRgfQkByNCP2HyLQCcrcS8n68XQMVhjym8eQe2CZaA7SrG90sXhnHumpkRhlifEDw9C1iQNxWGy41qzD1I3y61Lc/DtnD0rBCsYNuqqd4tGMjASzZnkq7Y4ZQ/1995P9n/9y6YwpXT7QFFXB7bKDJCFLMkgykiR36LnTQ7qx4rgVH98ZeK5d4aWI3iVzQdb7toL1oYhdja13QalBQf7ceEVrL5itew5zorCcQQO7Tin2ht5oZOj0WeTu201QZJT3JnQ+zqMQgprqavymTUNasgTjp5/Chg0t48v++C9CD28n6q4bUFVB1ZZSQgw6wmP9KV59kFgt80ajF3z/lA9vDJ3v+X/kMDjyFQQOgKI9MPQqCEtuN9S680UsYxMpen46ASsiML7zDP7fHUHo9MQc3EPDb55F+vADssNC0OWfZNiyZUizZ3tuyrW1nawDWQ12itfnej60uf9JUpscDQlMRfmk33MdNcmD0dsb2X3ngxRM6VxjRKgKCUqHCpFNN9Dk3R+wYs93BM5IwVzox8i9g9j5p19hKa1kzC9/yZ7//RkkCQmJmaMuhbIqWL+exH37oKSk3cNLTh1E0U+vov6mqaS9thhzx5tyh5t2w4zxhPzo1918Ed63bbnhf/7Pnm3fBslQRbEjk0MVbhRVoApPBoyloIh5N/8EaWgqcqOdqgdvp3HWZAyFZcRffw+j4uLB9Fevb4Z6Pz22k0U4SqvI2/MtY525NIx6ANUykxHP/QzTnxZgy6/Hbqpr2UZBZXvdEWr2fkddbRXV1nzMZcUE1hSzMaCS5P1OIisqOLRhA5Ik4V9ezui77sKRlobU2EjuHXdQNn48DQ0NmM1m9Ho90WUKl9wwHr2PjBGTUU/F5GlYP/qE+v+8j7J4GZYJFxEb3qpUOxU3zx3awC/SJrG//C2mdlTMmwgymClsrGTd3q8YN2I0g9esZN70Sa0DfDzQcjMqaTB9ihCiqRGfx3fYUGwl0pxAUGwIvsOIW4N85aIiAqrLqdu1AV1WNkENdVTvWt9exhwDlbZXOk8hNc1RWIlf7XGsGf8P3fFiAhpLKdv5GKEpN2GJmuz1uPtCiKX3itWRw8fYsyeTgJAQnA4n9dkHGTTwBz3a1u1ycmj9WhpqqkCSMJWVM+qxJ7ElxNPgcOD/6mveLRI1NeCyw5HloDeSXVhFzdGTJFSW8NbbLzFwchLXPL4KfXIqJfvKifzJrRgumwmVlcjh4UTEB8KWjeivmINpz15Y+nmvj1vje8xpjj/pNacz4LTXHFwqnGWHhdtWJ5RGq3CVlIvaD34jxA03iIqK9Z4xo0cL+6xxQr3hBrHlwdtFfXxcSzCp2LhR2IMCPePy8z0Bqi+80G4XX3zVw+C0joFgPgK/FHujWL9+pdd1dbs/ExUVFZ3ny8gQdYPSfO7z0Pvvdgq2UxRFHH3sJnHy498Le1VRt+IXfv16t2O6Y9VHL/V6m+1F28XmAi8BfTk5Iu/iyZ5/ewkS/t+avZ3Pc1MAZ3VynHAbDaL8odlC5OQI93XXiqqt64USGCgc8YnCERkjjv3xP2LdfzeIZTtXi2U7V4tPt30rPtz7QScZRESEqB2TKhSLWex/+DbhbKgTbrdb2A4fFrb580V+fr4o2bVLKOPHdzqEg2v3dn3wOTnCce0C8fH2XJFVWCPExIni2zWtga0Vtnrx5IHVotrRIIQQIq9yv/j3xmfF2sP/FYqidJpLCQsR9lGpnYOAO1xLbQNSD3kLVhZClB3MFuWHT3Qtf4f9dxeEbFuzqtdzNOSvFQ3Fpx7w2ZbeBpwKIcSe/YdF1pqNLQHC9uHDxMHf/FpsWfKh2PrpYrHt84/bfSf1FRVi62eLxeaP/yu2LvlQVBUXtk6WkyOU664TxVnHROHq70Tj0KFCcbtb1rWeAz8h3vtL++VTpgi7v1nUfvqRcCtucXjRIuEaNrR1TECAEMnJnt/Ff/4jxA03CHuDU5T/6Z89CsbVuLDRAk77CffQ+Tzw9e3Uu1SuL7yaGUnxNDQOJYij6PUBVFZuwHLlRExfrEdavwTXvTchyRK7dG6ksACMG9cSl5KMUleLpboaKTKyf3zFXfjvhVCQ3d797IqqoNe3+crb+HazH36QsV3ts0OwnVCdmEcMJ/76R33GW/Q3gYbe90IRCN8m/ublvs6nl+XFoxIoeONVJu3OI2T959RVfQ5PzUZftAr1ojFsevZBpoeMI3XhD0nZtqPFDVJjr2fZoV2d95GeTuZjc5h6JJBRBzI4tu1LAiMTiA1MALOZ+Ph4jxxtj6E5EDhuIBgk37ELBQUYv/maH9ZUeSxvU6aQsvR9dgz5OcMjY3nl2A6eHpmOvik4MaEuiNsX/I261EhcjhcwvfAyXH45iuIiN3sV4YPjCNma6d2F2MvYJqGqyLouCot5o7c9eHoyx8grPbFfp5tuYi0kWfZcak3ymQoKGL5gAfzBU8iwurSY9e+/g9nfDyEEZv9Axl9xDUazxevuZJ2OmNRBYHMg/PzI3rsLvV6PrqiIsDGjyX7qCUK/+oCAFRuoHBiPvriMiBGplDz7K4qL8xj55BN8FWtC/ORa9s/5IVc32D0tGy67rNXCdeWVMHgwxVm1JDx0F0yd0u/BuBoXLpry0QV6Wc9jc99kdWU9iruMsjw36oQEuHMJLR1h7k6Et5bBjBnM2LOb0leeJGRqMkK40RVIBP0ph8ZpYyG3FD5femoC9eAGryIj+8jcURQFU9tI+jY34oiemEzbxHXIOhN+KSM4+Y9HsAweQcyl3hrqnQLebtZ9QAjhs8hY5J5D3s/n+vVccjQbco53Os86nZlJgy+CwReh/8tfCBr4D8/NNiwX/A8hmfSYHQ2ewW3iL6rtNT7jW4VQkS+aAYvuYMjmybjrayi6dDoD1q9H8vV9p6dz8hdPMjwtwncWS3w8XH11O3eI9L93ySw/yS5rKY8Nu6hF8Wg7L++/zppVDzHzVz8ne8BzOBw1DI2chJCaAhi9ZX30pTlfX2u/9UfWSfMct1wEve3v1Fe6iFmRoH210g6Kb2j0AC6+rRe/sfXrqR0/nqBjx5A++4y0iVM8y0PCESGh5J3MZfRLb8GkSYSOmAOJ9fDJWtJm/ZC0zEzw+5Brp14LgHuSwo63vmLq44uQ9TrP7/LOOz1xak0i6nTy2anBonHeoikf3ZDgZ2Fqvg15QhypA4PJ3LaczI7FtEYNJ//53xG7bQcDcqrhp1d7Vgbnwty1VLz8DPtfep2YN//DYVf7U15X077IVpf04AavSjrKcjIoUvM7rVMaKgkyeWmQtXAhwU8/3TkbpSn2wvnH16ha/A2EJkMosPgbT0ogfgSFXkzZ/k8Q0TE4srLQR3TsIup5wrhP5FH+jzc8x1zeiJQytMtD1ZeXEpY8hLK7fo2uqoLoe+4j7xc3UbDhP11u15Ecay7pwy/zus46ZTxfPncv0cvXMPSLj4hqEyS86hdPsbD4UOfzrNrZmPktM0de3vlmu3494247BMfzOistkhE/vfe3VIe7mvqDb+OXEoVrfAjuCRMZcOerKAEW7OVF2K75IdK7iykzRgJgKCkiurwGqdbd86qbbcZdEZXGgAG+C9sFWaK4IvkxqpXrGDPm/1BcLva/8gpDLG3k9/Wg6aFiIIRA6kFfJZ/0x4Nu0SL0H3yJ+xc97+PTE/bVy+Q0NYBsJrCogJnVjRR9/SbRZfUk1FWRta41rqm2xoo+Pwdl5QpcIwZhPJFP2W9+ju3rtyi32yiKHNtuvq7idUP37+ei2lpc8fHYgoOpfPFFdlssIMCvuJgRFRVsjUigJuMIgy+9AsNzz3Py8iu5fM0aqseNJeroEfL++hDWjMUtc/oNOEZ1yhDC87I8C+69F775hpKUycQMaIoh6lVHaY3vPafZBdRrzqmYjyYURRVbP88Sqqp2XtnG352xda3HF9ohruLAySyx4/iB9ut6S1u/uhA+57LZHeKTDT0oWNVhvuP33tMpHqWZo1+t65GIVZ9u6NG4I4vXdz/ISxzB9i/W9mj+tuw9vlUcPLG7y/mLq4tFzYjBnvPZtPyDtU0FvLycZ68FyJq2W7P/C6/FqHKqi8WnGdu9yrj80Ovtj/fbb4UIChLihhtE0bK3PDJ0vK4iIoRjyHihWvxE9RsfibIDZaLsQJkoP1Du+csoF1Xf7RZKaLhwjp8iVIu/qH1riTi4Zp8oKsjzfrLaxAOo/v5iw89uF7s++EBsfecdseKVV7xv0+FcCiFaY56E75iPkn1HRGWWDzn6SI9jPtoUXWso2iQaCnp2ffuaoyNLczb53M46epDvwnk+Cprt3f1Fz+UTQoiNG4WIi/P8Oz9fCD+/TtfwX3KKve57T9kRUfLWq52u3/qMHaJi0mhRO/dyz+8xOVmIxx4TlYVWUVdp6518GhcsvXl+fy97u/QWWZYYe0kiWbvLuh/c/EbWBj+jDqPe+7o+42suIUDqwdfaIaOk9Oqrzpi51OEQPesX0rERXx8y+VRVdFvSW5Ikiq671Pv57Mt35qUHiaKqPsu8t9B8vNdfD4MHA6B32j3faUc50tPJ+/2H2F58Fcv+bZiCTBiDTBiCjBgCDej99ehMetyTp2P96Bsa/vAKhh2bsCoqdrWLE9nUf0h6803S8vOYsHAhUxct4tL77/e9TU+a83X4vg2bNvYt3bYrHD3oktyhZ5Ak60D0Muajr32H0tM59u+Xe9bbqc01VH8qPXxqaiAiokfXcIPLzpbykzhuvL7T9RsQEEnw0WzMO7YgpkyFN96AVasI1jVSU6r1ltHoPZry0UPM/gZMfnoqCjrUEujqxtu0Ti/rUFT11Dqm9rD7qt3lQteTe3qHh0HILi/BkKdCF8qFydQkYE8a03XViK8HCoxQVSRvylib86mTdOTffLXnfPZXl9sOyoKiCgKKin3I2+Z8bNoEL7wABw9CSQnhP37Yo3h5kcMQZMLvvjsw7dxIUICL4IFBnr+kYEKkGoIWXIxx91ZCF15KwLB4LHs2gbkKZ08etgsXEng0u3+buLX5vgP+/goo/VQXouk6MD722543vmt2Q0k6hNrH5n59aDQnQc8b5DVdQ4G6Xt6m4+PB4WhV3JvcUysrankuq5C/5pYQZ+roHoUaZwP3Dr+UxMBYr0q3fvpMGiYPZv+dD+BasRIWLUL377dx2txad12NXqPFfPSCqMQg9q3KQ5IkQmL8PEFWPUCWZQ4V5ONSlNYfqQBkPX5Gf0YP9F5boSv2HD2J1Wb3TNVcaAyw2h0U551km64eSZJa9tf8b0mSkGUZc0kJ8WPGcPLllzGWlTHgZz/jYFMAnCTJ6HU6ZJ0OnU6mpLgCx85CH+ckmOjoAABUm5O6NZ724FJJEeZhY7D99k9I5aX4PfArGl6PQh8R0nmS7m7iTXEE+uGdG7Y1x8Gc3LCeqHvuJTcmGlVVUVUFVVHILjnE6DFTvE5bXFPM3pN7sblsBJp7n0nTjialZefy9zgpFSDNvMZzrg8WIQM1jRVQn8uoyWPIeutlAkrKSLj9QdYlR1FxsoZs/2OE2us4VpqJadoQRhoN7Prn84R+/jFDuwvg9BX/MHky+PvDK6/AggU0/OjHGN9bhvTUQz06pIo50whomrdk3x4c5WWeImF4sjOQJY9i11STRpKb/i01rUNCdbk6T1xTAwgaSnPR6Ro8Y5vnk2VUdKCEIutkZFlCkiXkpj/hUhD2JuVJbYpbzatDP2EaDYtuxRymQ//gg7hjA1tqiiAEwu2GohKMq1chxo5COpaF66XnaDi2h5JgO5byIASeWiSe/8BT36/9dWkoL2bg2tU4p4zGnJlF4b//QGPZZqBJsRCCcntD9ye3w3dmdzgpcdSie+G3kJWBhIR+RCqRCxaS8kEEVvkZ8h75DbVzLvGca6k1k6v5M3h+58bKehKmTufEv/9L1L/+Qdx119E4fgKjauvxe/Q3TLj5Bswdq6cmJbH11cdoqSziLX7D35+AxUuRDy2l8frfoX79NaFDkomtd1JwtJqEoWHdH7eGRhOa8tELzAEGplybQkONk6JjNahtzNcGkw7VxxtUXNgALh4pN5mZpKYbhYQQbtYc3NUn5eNwTgGzJ4xoXdDG2jFjZAp6nez1bURVVc9ykwmznx/x8fFItbUEBgbiHhDrGaMoKIqCoiooboX8iBBGp3m/sRzeXdyifIQsuAi1runGGyihC/LDMnwgHGlEMhmwDB9I/dr9noqS0Lv0zEWLiFj5Nfz4Wq+r83ZsZYAkERoaiqzXYSgsInj+1SQmxWF2/w1efKlTRsiGYxu4dPilCARBFi+lqPvAsLQwpsRH4ily78kGcKsCuxKMy+RGr/sOtz6K/SWFDBAy+TtVnGUzORRez1y9gXi/CAz+NhiYwKhPv2PV1Q8x9LbrPA+qkSNbM3+WLEFa2RTU6CvQz9/fY8XJzAQh+DL9ephvJc2C79TPNlafsitmk3Td3Z5/795JyiWXIxRPFVhVVUH1VIYVQoDa+n/P78LzIE9bcEOrPG2+7+pXnkZE2ZCDJVRVIFQ3omnOYwcOkTDgYlRVoCoqQgVVqKiKwH68hqTJMa3XuySB26NkOGv0WHQ1noY+quJZJ8kggeRngtp6aGgEgx7Cw9At+4a6+BupUiA2WkVCanqYy55NPY/1du4hnc6AY/o4yv/9Iv6frSBw8z6UOdPbKSlVim9Lm7RnmdfvrDj3MLXFJ4hMHAYoCAEqEo7JEyn921+orBEM/8mtlM+82FOslWZ9XbT57JHBZHUS7VKpqLFhnT6HGMPzbPvLmwhbI+PuvYOHk8byf6nRTB3QPsvHYvDHoaqYfBStA3A57RhNFgLvv4fSV/7O5mt/DICwG4lNDUFn0IzpGj1DUz56iSRJBISaCAhtnzXitLkxhYRSUbmudSwSZnMCfn4DiQvrXLIaIMhypE9yWMwm4iJD+rStZ8dBsHUrEddd1/Lwj/LRkntvkZWQUO+ZGm3jGHR+ZnR+TeXP7fWwfSvyD65tmV+OCUcyGcDVdKPuKnvHi/ujJG4dnYpFNz3Qpu/eje6LL4gZmORZ7lJg9myO/vFhomtcJP/soU7Kh4REaEBoFyep9/gZdET5eckoAqgJh63bmbXgRpR9+9j5znvcd+PE1vW3Xe6ppeBnh0FDMfz295j3F8IPfwgff+xxWbRJ0+zW0t1BudPrJMIDDbjpcP570HROb7IQkDiw5yfCG22+b/Par9BfdTUB4UM6DbPkVRA1OryljHxb8hrcWIZ3yKIx18HurYSczEWffwI++wzj2Fmd91/lgqgopJ17oaAA3ZAh+L34LDHuBtKimsqbd9f7pD4STGH4R0yGn072uCyltHaZPZaqrb0+NQadjoj4QcS1zQLT+UNAEIMGj2RQZiYYdKQn9iC1WG6AABOXpUWBvhFSkpm3ZilcdRWY9bw2YzBP7jjB9vK6lrYOkiRRUm/j4lgF2p73DudD+XoZxgmxyL/8BQOA5rva8ePHNcVDo1doykc/YbToCbeMb7dMCBW7vYCqqi2A5+1MINDJfgQEDMZgCKHvxQ76gf4o2tQf859K3YamfWQ99ihDvOxDCIGuvr7Xvvke0+HmHHbvNZB8RbfyWt97H/26dXDj9Z3HtFG8JIAf/xj27/esa+Oi2qHa2banwOtu/IqKGTtmMlv+9E8Sv11K8IfLUH45HIMs41ZV2oV7+XB7dXQ59BsLF2J+/nc0VFWDj/YlvgvD+ZgzPZ36hfcT6izo2bXcFIgZ8PHn1Fwzp9NcnZSy5u85NRWOHfPElVzeId26acyClARQdV6Lvx0r2YzItHoORGqyrAC15UXkqNEEOOqaDlPCr7CYi9espn78GEKOHGf7Ky9Sfmh7S90aSZLwd5uJcUd0OEVmeOI12FOMoaiM5Nw8nB99jt/vf99iYfz95BQ6snnPPuoyVmIefQmyvk0BuDbnw//aazjyxh0MHDgLXW+LxGlotEFTPk4jkiRjsSRisbR/X3e7G2hoOI7LXUOAoYF1Rz1ZNLEhFlIi/NH3KJakH5WW/mwV7u0m3XZ+X/SgbkNXj8KKi2cz5Mnftj+G9esZdtMRgo7keIJaTxdtbs6pV86B27vPgAi+9cck/+UvLDuUxTXDu3C7NT+EvbioEiP8mTLeR72OMDeEWLhpfDyM/wVMmsTwgRZ2oNLodgJyt26vtsXZ+ltFti64HPMHS+D301oXNl07yQlxSDpD+4d307qo2FQwKr6runZ1LbcNxGw6ZvvIVNScQ96F7KiUeVNKOrq80tNZ+vzD/MIvvr01qbkj8sAoBqgrcD75MMq82a0xJnGQLPyavm/PPmW3EfeMGVjffQvx6eeMOHiI6h/cCIDaJNfJvRkMGNWqSAho92XJagDKjJnsufslLqrdidSFYmbKN+M0nqQhay+BQ730uqmpQZZkRo26jT173mLixJ+1KIlGo5GioiJiY2O9n0sNjQ5oysdZQK/3Jzh4LAAXN90fhRAU1drZdqKq6c3Ug9mgY3B0IGH+7aPTVaWfS0L3Z3XC9HR4+WVPIaKnnmr/hjj4IsIGx3J0ex788D74aINnm/CREA62f37L2Lv72Ja74zGkp1P6lz+w6u3fkPrRX9kZndtuuJp1AsdlP6E+OQGd3cGRn/yIsmke69XGcpXyXSEAlFjtyPUuov097pRwg8La/ctAkjAXlpJaW8TB/cvwzzpJsrvnbdDDf/oTot77D/fdNJuhZu+qlWqzE3SiitRRQzj44lNErVhLwP/eJjtYIcvtZgrelY9/bN7O/OpqNu3cDcCgOXMxPP0M+2+5BXP9EdaUVTJsWBK7f/tTYtdsJejff2Nz1V5uu/lRr/NV2u1s/npZl8cjyRJlldVERka2KIrBoSGMmjytkxvNuvBaCBtAp77R6enk/OoXjExI6+wKSk+n7O4/kzhM59VNtN66jYAVh4mfMgjdIz/h5E3N6z3SmEsqGDQ0kYwnf0z02qEEvP0Xtt00j632EIZJ6xCA3+5ifrRqFdZRownKymLv75+jYv1GLMXFDC2vYO+GjQScOMGIskq2rn6/3f4tJeUMK8tDPrCDHfJhhjbWsStjPRJgLiwmdexwNvzhVn4UexmWBQvg+v9rt32nOqs1VjCa8A+Nhrt+5nHxCEM7parGL5uw6M7dt1to9AOTnqhBoezKn8aw517k2OQfQl4Wjg7b2SoqGbRgNjWbl1B/wnPd6EoriVi5AveINAwnCqh+4ic4Vr+PecdKChqTSEj3WPkUJR9jUxE8DY0ecVoqjZwC52KRsbNJo8Mt9udXi7VHSlv+1hwpFe98sVq4m5tFnWbe/Xarz3XbV3doDtZFkzEhmgqRdVGoafNrn4ptS9d6/Vv3/pft99NmjozfP+tVjqz8LLH5xGbvRdm6aNb36tHWY16XVyk2FVR5PwEdmpV99vr73sd1wXP7v+56gI8Ccy8cWO5zk/9s8FLoqps5V3z695aPqqqK7Z+/0fL5wOefdz1fE+vWtS/Ytelb7zLmHlojKkqOeJVp34a1na+dpnUnv8v1el0JIcTqbb7PR9s5Wpg4URRmHxcfH9jYsmjVf9Z7LfTV8Xs+/IyXomtNY2xDk30239u041Wf8ncrb5vibc3s2rxRdImPOaqW9KwgoK/CZ/UFGWLXB08IRVFEZWWuOLriNaGEBQk1fZbP4msaFz5aY7kLCItRx+j4kHbLhBBUR0/g5MmTKIrSkkar1+uJjIwkICCgX2UIMPXSt9uFOV8OMFO1ZD1hEyfCrl2QlgY33ACffgqXX47FVM64a73EQXijjRl84Jw58ERnd4dTcWLUG7u37HiLe2gylY9JG4Le1ggvPu/d1N8mtiV+1Sbg/zqP6YIwg5fz2zaWpKrKEyDcTNOxxE6MhTmRXoMjTWZzr2Rg0SISl6+D6z1uBLfqRie1vT30wPGSm8u0a6+FsWNb5fHZz8aHE239elKPHoWcnM6uoPXriTryA8g97CM7qpfOoUWL8H/vPbihB7FI0O57Dvr0G+/bpKdTcsflJNX6e22+NyprL2Q91qPme15rznRAZ+/bHOq7y7vff0eazkfVNRNosBWSduU9ZGZ+iF7vx/DB8xGzV1H1rwcJt6V0G8CsoaGFJ5+HSJJEWEgwKSkpDBo0iLS0NAYNGkR8fDz19fUcP3683V9xcTFu9xno3NlMF8XBgi+dhC7AAo2NUFvrUUDsdrjuOvj2W8w+unR2SVPdiHY03XRdqguTbPJdLKxjFVUvx7Jn8Rcce+v97qtZLlxIwrZN3ReP6lAcLWHjTu/jmotyLV/uiVVopulYJKS+V9v08lDKvvmqlo8u1YW+TRNCm8vZo2lrx4zpsTxeY0rT08l981XvheXS0yn76xKfRedq3T3ok7R+fWuRt5QUrA8+0D6wtqNM3ircLlxI0P5dvr9nCe+FxNLTyXjzwZ5VOPWGl6J6Si91zOZ5Qu/7UdfVhb0ghEr1zGRMi78mYc79BAfHMnr0LQwfvgAASdIh6yzUnlyrFR3T6BbN8nEBYTAYGDCgfUqvEAKr1UpeXh6K0lqHRKfTtVhJ+r3MdTNdBf/t29csoOeh/8wznofV/b/s+fxtLCxZv36YcV6GOFUngTrvBcT2VRcSMGEU3/7tDwz66jvCP/0vW5M8Y/MbrWBsSsWQJHR1NT3KmDk2exzimQcpW/QDn2MMRaVED0tm03PPYS4rY8xjD8BPf+N17K5jB6jZsZkJ9TWs3bKc1qejoCi/nKLKRnZvzSfwxFHGWh1s3OZpKLinvBbRFO/hGd2Kf1ERl91+K7UpKehtdjJuvx39mJGU5hSxNGAp4Dlv4ccKyVy6FEmSqKrMZs8e70G7UVHJxMePbTlXQPeF47o7lz6uHTW/gurgeAI/eRbr0IsQgcHIZcUEPvpTpseEUCceJfu2a6icNBJZp0MgIek89T7MJRWkjUgm8w8/xVxRxciH7mX7P3+LHNgasGmLifdubcjNbSdexbz5LQXYfB6aF2vbhvpK9o0dzA+e/4wvr5mAI8R7fRlnoxuz0l4XCiwqY+qoIax88VFEyW5+8PDPOfrcExh3mJAlCRkZWZLRSTISMiGN1fi7XZ0UKqmkBJEwEPfPb0CqrMZ8793Ynm8OBhf4X7MIydL0m2mjpBZteYeIx/6CKcpHkPP69YReVYLYt4/afz+FWrWZ0NCpSFLP46A0vkecXg9Q79FiPs4MLpdLFBUViWPHjrX8HT9+XDgcjk5jP127y+c8XcZ8COHVT13zj4+EmDVLCINBiIAAIfR6IRIThZgwwWcjsk5s3CiE0eiJ+UhJEWUzZ3odtunoJnGi9ITXda+t/KzrZn1NPvzqiVOE289HM7AO7F/zSfey5+SImsljhKOiQIiMDFE1JMnrGBERIcpHD++6EVmbOIS2Yxbvzu9y/21jXSpGjPQ5VFVVoSiK2Lf/A59jdu/+rGVeR3BwO3l8xXzkZK4SlWVHvcqVsXWt53Pba6dp3ZHF63yuK3v9H0Lk5wt14kShut1CdTiEYrcLpaFBuOutwn3ggFCvuUa4KiuFa/16oYweI8oyj4itq1ob/n25ItvncbZlxzLfcVA5y9/0stAj45oDXuT3wttfbxJqRIRQ09OFOnGiUJZ/LZQT2UK94XrhVtzif//9m1DGjxdWu1VY7VZRa6sV1Y3VovLQbqGEhwv7jGmiYWi8EMuXC6Gq7f9OnPB8/6oqxIEDnviTpnWNW1cK11HvDRDz1/3d6/K2xyeEaIkNcbnqRUlpN3E4GhcUWsyHRrfo9fpOVhK3201+fj5OpxOz2UxCQoLXQk9d0oWfumDbUowiFNnapuS23Q5uNxQWet4Sy4p7vq/IyJbCW2FpaR4Td1sLS24uE6deiTpsGDg7p2cGdDw0b3Eh6ens+vM/Sfp6KWn9WAsl6EQhjlmzUU/kUfePv7B2w3/arfcrrmDUiCRW3n07txPmu3aFj1oqPTZ6d2OhkCSppYR3T6gdO5bIdeta5ZntpdiXL/mar51t6zyf28Y4NK/7aH3ndW2pqUESAnQ60Onayx0YCFu2oL/mmtbCa+HhUOqprXHwWCUN1p65l3pNs/wZ3cjfjCQhtYlpkhYsgE8+gfUb0M1K54d7diMv/QJ/U4dMF0sYzJ6NackSji77HUN++1u44opOc7N+Pcyc2RqX1WSxko1GRHNJ/A41bEw/nAzpPTjW5nYIDz/sKbXfTeE24VZxFloxxvojGdpbSYQQqPVOXOU2yM3F9MN5MGIEkr3Rd7q1xnmBFvOh0YJeryc5OZkhQ4YQHR1NdnY2R48epbGhB91Ce4BRH0bo+HHYLQc8C8LC4NprPQ99vd4T29AFtYe3sOa3PyLr81c5+ulrrStqanAFBXnt3Fk3aSyVy5Z4jUGoj4/pURM5t6pSd8MPvTcD6+CHD9i5r8tjaEZKT8d88Djq31/F/7MlXD3uOq6fdXvL3+VTfkBcxECG+Y3ovhFZbi7cdx+89hqMG9czH36bWJd99z3oe96mY0v70ZM9jg0AWmT2y8ry2fyv5ypND1i/npA/v+w7dqeZDvFIEnDguIOvvjtBZmY5N13vqTDqqHNSe6IWe40norP6WBUnV+RSvLUIpZsus/0e7tBWQWySf93jv+w2bkRXb/MtjI+4LMnQRvloHtcUwxP8zlc9l7lJkQ8OGkdF5XqUGZO9xgK5KmzYj1Shj7DgyKvHdrSq5c9+tArHsWoUqwtTYhDm1BCYnY5r8dfYX34H9ZHHcZyo6blMGucUmuVDwytms5lBgwYB4B8Zz5ojpQCMSwgltE3NEbfNzY41OT2as9RWw/KSnzMkLJx5F89n0L79WPOLKf7LE4xcvRo+/hjdkME+tw8aNIno5DRqS4uxKw6GtCkYlf2bxxnRVY2SPnQgbUZVm7IzfGXMtHlDjZk3G379+x7Prb/jp4S//i+yPnqFhOt/hjmsfcnPlsdzd9k66ekwZw7k53taxf+zm6JqbSwmMavXwt13djnu5MrXGPabp3r3prloEZFr13gvziUEum274NohnkwelwsefxyefLKbSX0oLOnp1MydR2RIcM8qnDa9nQcsWsScBH/SLvXEfdhq7JRsKMQQZMQUacGaacVd70R1u0m+ehDWIis5Hx/Hr0omd/l2z3WhQNi4JIITfJRr7StdZI1lXTabS+592ns8VdN2yXt2wtIvu95Hh9gayWBEtdV1HldT07U5zYfF0wSYwtNxuN9C1zKPp++P/Vg1On8DlpGeCq261JCuZcVTS8YYFwDVEph06CP8aDxQjnloGLJRiy05nzgl5ePFF1/k8ccf5/777+eVV15pWb5161aeeOIJtm/fjk6nY+zYsaxYsQKLpQ+ZDBpnnfgwf+LD/FFVwd78amryXIT4GRmfGML0+YN6PM+HB04w8Ltr0TvrOKy3ET9iJOGxMWQVl3Ji7nyCD+whb/0O9kxs7UIr0VyYvomR1wIQELSfvIN1bH7xTwxc8S2Bx7J5YvkuDhY1EmHSMSfeH/+SIuZt20vAldfDwUM9S29sS9MNdXhNI58cLmHz5PkAFC7bytggPwD8igsZV17L5nX7CTqRxRy191lF0h13EHtgL43Tc1qVj+ab+RfrPJ+9mOm/W7mTkB//AuN320gsKicrbjgWt4WU6s9wNpaCjwJk7Vi4kJg/PN/ZZdUBua7RZyryoIHRID0Pzz3H4WefpaXU1D33UOLvR/yXTW/MHRVACYiKAkWBlBR4+mmYOBFC+pLC0Xo8Pa7Wu2gRvPsezPohAJWHKrCerGfgVcmd3I2ukhIAAmIDiJ2XiDHEiL7JRSCE4OB/11MVlgsS2HOcuHaswTC5Q9n23tLRpTa4jWLeA2W46NnbSPChiDVW7sOW8f8AMF2ahPTc7djvnIdwOHHmAxXZnQqMFT82h+66+5Tt+gRHfRGSrAchcDvqMFWrRG3ag3vCGHRHsnG9+i7uAxWYB4ciW3r5COqgkOmCjFhGRrRYTwxRfr2bT+Ps0dfAkh07doikpCQxevRocf/997cs37JliwgKChIvvPCCyMzMFEeOHBEfffSRsNvtPZpXCzg9P6iot4uVB0vE6sMlot7u6vl2tTbx5j//J5Z+8Z3YtHVPp/Wv7M/r2UQ+Cm853Yr4v8/2iopGpxA5OaLwkovF8fKi9gWjmnj16JYey92W/x443F6ONkGfFS8/3Kc5nQ1WceKL1zot3/vPD30WZNu+dK1XGdzffCM+3OE7SLjjudv+m6e8Bz+2mddtMXUOem2aZ/fuz1qKtK1fv77dkB3/+bfXoNgTGStF7ZcfCBEd7ZHls8+EsFg8BeOaA069cGTx+s4L2wacCtFtMGdbbPlF4sSqA6Joc6Eo2FjQo226Q3E6hPPQQdHw5XJhW7Oq5a/xu+/Ess3dBy0LIcTbyzd3GQz9+ravvG/Y5rvN3PqK9+J6QoiKA16KpHUxl/jgA1H9s0t8Dq088J3IX/eaOPLZL4XLVt9pHvc1V4m6PXtE3T1PCfXRR7vfdw/k6fh7dhTWi8YjlUJV1b7Nr3HK9Ob53aeYD6vVyi233MKbb75JaGj7rqAPPvgg9913H4899hgjRoxgyJAh3HjjjZhMPjp9apyXhAeYmDc8mlmDItmbV83KQ6W8vSmHZ5Yd5ONd+aw9WsaaI6WU1dmps7f6kMODzNz10x9xyeUXM33K2JblJysbOF5a3ydZdhfX4rz1NnjzTQw6md/OSuOxVYc9K6WmJmUd4yZyc7lz6pW9rnXglSb/ufrPf2Lc27cuxbayk6iKQs5X/yLn6zcpXP9Jez+/19oZHfqObNoEzz+PfP31zP3Z3b6Pq4OJ/NjNC7t252zaROHTP+k6xqArt5av+AIkKCuDL76A66+Hn/60b64xbyb/HrYJUJxuKg424hcbQNwM712de4tsMGIYNhy/q67AfPHc1r+5c0kwDmbbzgK27Sxg+64CqmttnSfIzWXhzVd63vKbv0Nv9UZ6Ql+368jChVg2H/UZd9RYdYz49HsZsuBv6M2dixzqDCYCx43DcN+dKEu+6L4WTg/k6RgHZYwNwJQYhO1AOartDNY10ugTfXK73HvvvcyfP5958+bx+9+3+rfLysrYvn07t9xyC9OnTyc7O5uhQ4fyhz/8gRkzZnidy+Fw4GhTQKmuzou/UeOcRa+TmTnIY2gXQlBnc+NSVUL9jMgSrDhYQniAiawyKzeMj8eol3G6VT7fW4ifUUeQ2YBOlogLsdDoVCistlFo92QcxJmNvnfc5oHz7707+bZuEP++dRJLVh9mRHgAj05LAWtF0/O56YHWwUxdOHkcad+u6VFL+Z6g3Hgj5t8/1a0LwxtBScMJShre8rlw8xfYSrJR3G2cTj2JW7nmGnj6aXa8/GfmD0rt0XH1JPCz6vKxJN71Rmd3xvr1DM46CFn5Xt1a9XU1uN0uz42mjUvE5aoH1Fa3i14Pr74K3/ioHHoaEELQsOkoidMHE5zkvd5GfyLJMmMnJrV8disqB4+Uc+RYJSa9zLgxA5Bkz3dRMHk6g1Yub702d/ooRNeRHlRF7Su1N4wj6hT7P5mHDMR126003PMo0pOP4zcqte8CeXE7yRY9ltGRnniSAKMnPkTjnKTXysfixYvZs2cPO738GE6cOAHAM888w8svv8zYsWN57733mDt3LpmZmS0BjG154YUX+N3vftcH0TXONSRJItivfanwy0d60nljQyz8Y102I+OCqGpwcsXIAZ2a5QFsqW+g0O5EAIesNmJMBmyKSoHDRZBex0UhAVjadP29+eM93D5pDHNSFR5ZlslVY2L56dgEz8qIAHa+8kdGNT9bO96IVQdf5G4m8OgJxjvqWZ+7uWVV494TLLjzfmpT09DbbOy795cUz5iJhISvXAdFUSgflUDAwz+l9sb26Y0uq2BASBJ6gxdjo7UCc1Beu0UR9bVs/99anHWRuFauwj58FH45WRx/9HfUNsWBGC0mT9zFhAkepcRg8HQTdjgY9f/+Cr//vXdlpUPq44A7fgqjhvo4Kg+yZPKZinzsN7cw/qgd1q7FcM01bNiwofU44gdSXV7RGgfSNIdu/niQnC1zsGQJmEyeMvuLFnYpS3e4a6248isxpsais3hvDaDaHFR/songWcMwJfWPxaO36HUyY0ZEA1BWZ2PjljyGDg4nqu2gngZKe0lnFSG+h9cWVxI+ChTFjk7nI8amgyLjuON6ctz1kPP3dsOEEDisx3zvrMM8hqceR+dWqP1iS++Ujx4qVpIkoQ82oToUr+s1zg16pXzk5+dz//33s3LlSsxeekeoTd1Y7777bhYtWgTAuHHjWL16Ne+88w4vvPBCp20ef/xxHnrooZbPdXV1JCQk9OogNM594kIs3D+v++DUnw9v/yDItTlIMBuZHBKASxVsqq7HqqgM9TczyN/MY7PT+OuBfOoqbWy7t3NNCdHFjTth137S/u/RluC1a5MualmXmZmJec4czE1ZGnMXLICf/7TzJG1uiIqiUPazJ0mYcwmhHYYVbclEHhyPOSKk0xT2tath1k3tlhkVN4ON/0ZXVIZreDyWd/6HbsoMhr7zGkRHe2ocXDrb89C5+GJPF+F77oHCQoTRSMIXX3jcGWazJ1Dxb3/r1CG2OQNlzOVXwM9/0uWxSZK+67foJqvGtA6WkQ0bNlD2619hWdFk0UhN5vNjXzFgq545dScYrriottVgaCwnfMEClA8/4NCll3Biq6dSKzIER1gYmxBCsLHz7crlcLJuzQacx44Sc7wG+b+rEA4Vy7gE6ldsRTRZ39XGBmT/AGR/I0qNDaXORtj/XYzO7xSCW/uRqCALUTMGsutAMZXlFQzcvd1npotPOmQVGV6+xufQANtQCvcsRVUd+AUlET54is+xTms1ssGAviYSvYgAoYIko6puJCQkWU/FDgvWMeUExPvubOuotYKiYgoLonHnESyjugtf7RuukgZUh4Jp4Om3Zmn0nV4pH7t376asrIzx48e3LFMUhQ0bNvDaa69x9OhRAIYPH95uu2HDhpGX1/7NrhmTyaTFg2j4JMnSem0YZImLw4M42mBHluCb8hoCDTqem5bKL9ce5a/bcxkb7IdTURkcHUh8qAV3o2Dl7rVEBYR6bpQShO7PZObPHsEIsHUrBAfD22+3ywqora2noqKCwxs34nfiBIPr69nb9EafBCTecEOnoknNTf684lB9pyq6GjstknR6Yi75CeTm4nj3K9y15egvvRROnoR33oE77uBYSABqTg4Dysso3baJAWWlGOrrccTH4ed2YxgxwvM2vHGjJ1bEm/vlFFKQO+Ej+2Jem0aBJfVWcnPWEJYwmvCjX1C/+mvCn36ejTVF1LzwCyaPTeHG41lwg6c8vaqoFJc0sGNfCY2KwFheRc3y7xg+bTIBIcF895e/MeuenxB4xTy27SogfGKbDJ/xw9rJIYTAXVKFLjq098XzzhATRw8ge1MppSMnsezVfzH422VEfPQFW2LaH0uJvgt3QtN36jL5ePgKQVBwDObxnmycyuPbKdz9KYFRI/CPHkjlsW047GXoDQEggU7vj1BdhKdMw+gf4nXK+sIcKg+epHDzIYTqSaWVZE+BuubPBosJxaWAUHHmu4m7agBGt4qs77/vwlXagGp3Y0oK7rc5NU4PvVI+5s6dS0ZGRrtlixYtYujQoTz66KOkpKQQGxvbooQ0c+zYMa7oWGVPQ6OPDPH3vK2mtnlr/eKqMS3/VlTBsdJ6Vh8upaAwkEvi5zAszIY0ZTIMH+GphQEIgx4pNQ1OnIBlyyAiAkaOhOpqpmYdRXYqzLjiCnC7UT//nBkzZiBJEjs+/phEL7UrFEXx+VDTlRVhGTbHM3+HKo9SebWnEJePCpDGkAFUHN+BGTwP+LffBiEozjrGuCGjCMg8iP8LL6E7cAAlORnT4SPUDBlCaEKCJ1DV5aIGhf8e2wZAYEExN65ZTfWEMUQcOc63LzzDLX35IpotI809X7qJL9hwPJu92Tl8eNcLGPR6uKJJ2ft8GS1JqY+1jw2TdTJxcYHExTX1GpmWQKPDyYGNW3AUFiIfO4q/f4cqnz6QJAnDgN7F4pwNUuNDqPM3MrleMPTuOwm9ZBaT4vzaWZReO1bQecMOaaiSOIJwq02lUSTP/yWQVAXa9FspsvsxasIN1JzcR+nh7whPnYEpoHfnSZZlBl420es6VVU7/S5Ut0pNVg0Fa/MRqvBUdJVa9WClwYXOv73LTLgUJJ2EbNQROSEac3D7l1ZXWSNqoxtTsqZ4nA/0SvkIDAxk5MiR7Zb5+/sTHh7esvyRRx7h6aefZsyYMYwdO5Z3332XI0eOsKStr05D4zSikyWGDQhi2IAg0gdHUXa8Bmt1DUHp6R73RNO1KoTAMXsmxqNHISYGKTzc87DeuhVxyRyU+VfgeumPmC+bj27DhpYy1Q6HA6fTSUNNLe6cPEJcbiora7FWlPl+o5Yl3JOnIX/2GXJpCdIPrm9RMAQS4qJpSIv/C+W1nsyPZuWj6QEv3voNYv16RF420oFDYDQx5g9/IlBvgKFD0b3/PtLChegnTMC1cSNBx44hjh8HRUHS6Vj/z5f5xeCpnjmNuTBnLpamGhLjV3x8+r6MJhbv3MO4pIHcf/m8U57Lz2Rk6rzZAJReOhfOUSvGqRDkb2T8tHgOHi6nbOY1RLz8KmHPP911E8gmhbju3X9jfe89gm+5HsfmNhlKbQxcxtGtynpiYiIAIQPHEjJwbD8fCV5/E7JeJmxoGGFDw3o9n9vmpnRnCe6mjBZJlnDZFWKHhWFJCzlVcTXOEP1e4fSBBx7Abrfz4IMPUlVVxZgxY1i5ciWpqacQ1ayh0UcsRh2qIpB1EoKml8CUFMjIQHIrGN/wpCHm3XUtAw+Vezaqq0OW9TTYKynOWEqivx7zqlUtmR6JiYlIGzfSMGU6Mfm57H/pNYqO5lOxcx033XGbVzlChybgarBRtmkfuoNHSGjj6jAMHYpaZ8W+cgsmnRN9BzeIJMuYBo7FPjSR2r++SMTSpfCPd8j9yS2EVNSR8LfXOfrynxi+bx8cPowuPh43cGLsME4s/AGDvl7J0N374Q5aA1Rl2eOSee45Buw8yEffvs5Nl/u2XAQHJ5ORsRi3u4Fx49pXQ7VYgtmx4yMKCuxERCS3W9ccv+V0KwyJ7H+rQ/SAU68q6rI7qcspwlZdi2TUobeY0FuMnv/7mTGYTOiNRmT5DFXQbFI4TcD4kTGIPz/J8ZwqTmaWMn6U53jjLAG8dmwbrpOCK0+6cYaWEF9ZwSfLtmCYPpLr/vRXNt7+CFfMGIVB14VylptLcBd9V85F9BY9cbNa3Ws1ZY1Yqx2a4nGeIYmuIvLOAnV1dQQHB1NbW0tQkBYwpHHqKG6V2m0HCboiHd2gZNi3FwARGIBUV48rIpTaGROI3LQPhgyBvXs9jcmMRoTdRsHffkOCPRTq6jzxDLm58PDDfPiLp1hYfAgOHMD5u2dZuXoz84cleW+i1ZyNMGQI6u49yF8sbY0xaVrnHpgMmRkceu63VE+a0O4YTCUlpP7jTQ4+8xQj9/yL8OdW4nrlSQw/exLJrdDo74elrh4hSSDLSIoCAwciNe/X7fbURaivh5//HPz94ZVXPC6jRYs4cHATo//+QbfnMiNjMaNG3dxpuc1Ww4kTOxgxonM1zc0ncgkwmRkT1wtFoZtmZN7YvquAKW1jPjrQUFlD1YEcFJuLtmYA2aAnID4Cv/AQFIcbxebEbXfgtjtR7C4UlxPF5fYavNzWDtG8Vqo8RnTcgE5jAaSAAIwjRiL59cxV1Nh4Eru9kLCw6ezc4+krExhmYUSqR5Fb+r81XH33DcjDByNlHWfX/U8y8bUXICQE1WZj7cN/IP2XP0bvSwFpupbbuRB7mtbbhswvshl57Zl/wayrtFFbZiNhWO8tKBr9T2+e31pvF40LHqXchu5QBeqYKeRcdT0pmT9DdqlIDY2IXz+C4S9/JXDvPpg2zRP78be/weuvw5EjOP79Bn5b18K/Puo0rwStHTwfegilKdvLay+TNssrH32OyI5lr9PTKXv5TaQlnzC6Mgdmd3iI5+bCkmXMvvhSDpeeINT5NbrH/wIuN3z9NWV/e4WB36zAGRiEzagnpKISqblmzqJFsGKFJxj05ps9iseSJZCZ6XGy33MPR3ZEMPoUzrGqqvjyCNTZHUh9ecXxdR57gMvmoHTnEdz19paYB2OQhahxgzGFdH7wO1QVUz+5b0q2l+OMCCUodXyndWpVBY69e8DZlGYs0UZrkcHshz4qGF1sHJI5ELM5jqqqjYSGTmXS+FgAcvJq2LjlJMERFuZMiKdg7GT8bn+ZyMtDSZ59CY1TLsLvq6XIBQXMvvY6Vs65hMtHx3Yv+CkEH5vNZ76vSkOtg6qiBpJGRZzxfWucOpryoXHBojqc1H2yAjkxlPzqRmJlkEaOoSEmlcD849jSYrFIMlJgIG63m/qjB9lZsIOwaUMY+0wZVFai3nQDfn/5i9fCYS236UWLkN9+G0ZNbi+Aj5t54+zL4Lf3dirY1ehQcF5xHQNun+9ZV1/f+e0fGDb1cpzzlpM5fyJjXnoTVq9GOpELgE5VCaypRdXrqI8OY/22L2B8HIy/AwDL3u9IX70S65jhBB/PYccLj1K2bSlVxwo4mvMxkizRIOs4NnQEAvDEAnq0Ch0Q7vKuYQihInl5eLsUlaq6eq6YOqTL76pLevhQlPOqySn1BGLqjAYix6dhCe8++FAIwbuFFfw0wVNho7CwEJvNRkJCQp8y8WKmzCd72T+8Kh9yWATmi2Z631BVEA01uIsrcew8AG5PR90IBuA4vhZdRDD61CEkJ4YQFW7gwMEysgrLGRRoIvCuUZCZSbhFT5VbYeWefK7SVQOCzWVlzLGGYAzw0fekOVB1715PsbfZs895F4zd6qI0p46Usb5TezXObTTlQ+OCxZV3nOr//onAm66hOHM9ASXZbPzwTWaYDBgGD6RyZhrR33yB8fHHKc3azsBylfGRI9mY/SVjBw6EN99Euv8uahdcgqVtCmlzlse6/Z7PTVke5W99yHclxaSvXEXDyDGE5Wb7rtHgJS3VYNKxLLec0jnXYHz6T+TMSGbO6GHs/uNvMZeWM+mhB9gY8TqWohKGN9RQYAxmTFkF/P3/Ee/0BN8Z7DaE240EhPy/17l2qhcLytzvMDUFm04/cACmXkeubR1B0VEEpaSw/tNlLBiahhACmabmfkKgAssLyr0ejhDCa6XUzSdyGJuc2KPvqxNddHXtyPbDR7CF25gwc3Kv0miPldazL7+Gn05srS1UWVlJZGQkRUVFVFZWMm7cOHS61jf77ctOYK22M2JmHDEpwbirqiksOMTA0a11YmSd9+JmXSLrkALDMQSGY2huItfkfhIjRkBNDa4f/xh1/FgaS/MZEDwcR14hlk0bcA8bjS43C/GP1wl75BHm/+Jm1D17+Pj+x3hgdDwl2w6jOt0gSQhFBQTBqbGEGFXq4wdT89u/MvDIVo/Fb926fqv6ezpw2twUHK0mdbymeJzPaMqHxgWLadAIkr7ZQOkjPyfJP5SKm3/I1Zdehrkkm72/vIaUKUMRi3eAQyH3sTtJGngZQUDlgAjU3buRZRnJXkHVzfOJGX5lp/nr7O0rKN5510LPw+KSeXx7+8+5tr6stb17V9UZm9cV1HDtyFhSL/P0b5E2/IvY0ChiJ87xuEj8Arh64hzPPkI/45qLr+XkhNepnpxM/Ls7CC+thuBgRG0tkskEy5d33Vq+yWXUePvt2HJysESEs23lWiZedTlGnXczuuzDtaKqCpIkc6Ssgv0n86iur0cgk1lazoLJ42kq5Nk7OnZ17eJYYiIiOCnrel2/Y3B0IIOjAzstX7liJSMGDmHopBG8//773H777SguFZ1BBgkqCqwc3VZCVFIQ+rBQBoZd1G57IXzVwW2iNzEt6elITe4n44IFOO68Fcc3a0gaNhvl5ktRERgCTLhvvwP3B0uR41IwHDmMlJLMwv+8TpklCDFtBolDI5GnTIERIxANDdTddS9FxmDCgiwEJkRzMjeWhOJipIoKpF64YNwON1I/1urocl9OhdzMCgZNjO4680fjnEdTPjQuaCRJIublN4hwOrBWVSHr9fh9t5LpOh0uVx351x0nMeEODuz6M78+KZMe0MDM8Jh223u9B+fmctvN82DsaB8PD6l37d1pute3uYeXV5bjXLOSxnHDCTycjWwy45o+BWd1BaW/vAH7wU8YaIjhkwmTmVyuZ+Z/lsO0aZQkJRH73nvw8cee4mJd7Lt09Gikp55iyOLFFGSdwC8ykuDg3gd6K4pCSU0j24/t5P7LL8Fk0Dcdkzj1h0QPzmN0cDA787zUvuhIxhKIHglRvsvJjw4KYuTDD9GYlAQNDUy44w6qiwvJzVSoKrRi8jeQNiGKETPikL1oY9WHNuMf1XWV5i+3/okJQ0M5+vyVxDhCGPLIE8hDh3ZWSIa2kbNJIajZv4KgSdcBYB85hpoyB34v/T9CH/kFeocDoqJwhUyk7u5HCXvhQWK+eI+Ge28n/7PVJM6ahfTpp0gFBQQvWEDwX/8K2zbi9/NbCauqQnU4cA0dgdHe0K21qbSgltwjlaAIyhvdHF5xokMMC56Luh+VBKEIApMDKT5e4XW9KgQmnczk5DDfQbYa5wSa8qHxvUBvNBES0z4DwWAIIjnpXior13Fz0mh+rDeiqgKdzkV1zRbCw2Z47ps+bp5lYyaQuG5l1yZqH1U/vSFUkHWt+6rSpWOcsxfjkiVk3HUvg1d9QeX/3uLQskOMfvYpcm77FWEFZVysi8ISnoBiNKLPzcX85z/j/vhj9GPGdN53GwtMxeHD2BcuJHHuXEr/tpgj4UYu/b8bupWzLY3OWvyMwewsX8+BhmCunjq5RfEA+u/ttJvzaDYacKnd9/KoH3IZx6qPEVp7glBTKKHm1kL4ublv4OefjL66Gv2ESMTHbxFcH8GoBQvgkUcI9Z7A0g63zUrFkd0Muv6+LsdNSboEk2U3fuZQig5tIbQ6m38sWcBdQ4PJ+9MP0JdWMPyhuzn6/B2MWf0tjcMSCcwtpfqJO1HrylAiEkHvuTTDUsOoP1lIQ2kNthmzCd2+FsO4sQRfNR3b4lGYt67GPzIUadJgrO9U4Gd3oaupAYcDrr3WE4C8ebMnO8rtpuLnvyZ2eGwna5NibaBhUyZl2cVUDhlNYKQfk+YknXPVYq0ONxuOl6OoMC4xhIgArYL2uYimfGh8r5EkmYiIOZ2W19VnUly5A5fsT4XNTUFtfUuqpUCgq6vD1Zzd0tZE3fxw/2q153MvuoqqCNpar6sanTS4FPyB4mk/YNgn/6O2xMTA5FgCI/wInjyK/JSFDImrwzollaqv44nKyMBQXEztD28kPD6uS6WnZMMGRt59NwDZziqmLbir/QBvroGh7V0UHx94iUj/ARgtaaQOimVYf9Xy6EN31p44Cd7JfIe7Rt3Fmrw1BBoDmZ0wu2WdvTyYhrICDEWlRDhd0BjY6wyQE1+/RerVd3c7LmrAONh7gin3vdsS0/LMoEHUr7wFSZaZkvwD8PuKiWNvg7kZfP2TqVxxuBFzVi7K7RfjliQq8g4SnLkPR2IakffeiuP9xegmTydr2TpSf30H7mnTsORmYX/uz9g+WIN/rAXLiUPYx07AdDKL+kefIXDLaiSdjCRJuDMPIRnNDFj6H3ijBBwOxNfLabjuVpwjJyCbjQRMH0F5TQXT5qX0+JycaQJMeuYMjUYIwZ68Gvbm1RAdZGJUXLDmqjmH0JQPDQ0vBAWOZF95MRsrKrHr07CXV6JrYwWx1NQxJmNv75t/eaPpIT8gdQh6eyO8+DxcfjlXpkWRW2tjBDB1Siyy203qD+ahr6ki/7V3OeZfjNU8BHe1nVjHCUK/XQ5AyeYtJL30J7BYOu3K2dhI1ZEjVGdmEjFpEgAVFZXoxw0n0N/LG2KHdFf3R3+m0VmHnzGI3Kp9JIcOJz31R2TVV5BZW9L3c9BPWDdvxjJqFDofNQaGhA7BoTgAuCiuNU7D5bBj0g8iedxEyM1FZP4V++XXQ3YXQcMdKN+zmoihE9GZOp93rzSf27/9Da67Dmn0aAJ372bUD7OgvNGz34IC+PJLLsnZjb/bjFTXQN0dP0QNCcZYkI+usZF6AXJwGLWvvkHGM6MJVVRkeyNlDkG0vz/2Je9R/5e/YMypQIpLwZRzDGlgIoGv/glXRBT6ogJQBTpbA9LYsQinE+eQ0biGjEJXW4H/1x8Q8OyvenZM5xCSJDFhoMeyVVJrZ9XhMvQ6ianJ4ViMZz41WKM955a9TEPjHGJW5ACeGDKSH6UmoQSHMCMpkXkpA5mXMpCLEuMpnjQVNm3yuAPWru2wdZs3rNxcT++W2bNh0iTMu7Z23ll6OgUfLqPyPx964jSAYRNHsvrlfwIQFGBCvuJyjIV5yG+9SeKRHUyLGMLAA5Wkpk0mceHT+CV6MiTM0VFUHz/eMnVjRQVHP1zM4ffeI++77zxz33orMWFhEBmJLj2dyY//Gr791vfJaLIARJlDeHLTk3x9+O/k1xwhPfVHALhVFZ2XbJczjfPkSZTaWp/rQ8wh7C3by7yB8zDInoyUmtISju/YysAx41rGiRkzKHrnTR/fbWdUxU1NTgZhI2d0O7YT11wDZjO2l56lbMJAAsIT2+83MpJd/30U49crMagSA77bT1zKdCIHjEQJD6f66//ht30LA7av5dIR/kwaG4McHcWmf72LafduQvftQ66q4mRpFnJEIMyaRc0Lb6Ga/TFmHUUSArnRihoagTiQQeOca9CH+uP/9iuYn3nsgrAWxASbuWR4NBelRrDrZBUrD5VysrLhbIv1vUZTPjQ0ukAIgZ9ORi9JHKjv3H0W8ARErlrlqQXii/R0Twrj558T8v4/ve9LFejqars085cVlLKpQca6+BOkBitzHrmb6EHD2vndE2bOpGLXLgCOfLiYkm3bSLpqPsNuvZW0664jpk1X6rqx46j4+BOkzz9vUXra0ZzuOnUqPP88s2JHc8XQn1KuH8XMlNZKp4J+CCztB8J+9COMCb6DPacOmMrcxLlY9K3WiRN7djJ85sXtyqe7XS6Co6J79t0CJ797l6R5P+q74OFmKv/8IFGBSa3F61at8sRjTG3qyVNT41FiO7jSBIpnXXi4R2lpS9PyhP37GXb55diqqqkoycc8byT6BVeDwUDt4ME0DBuO9JM7kBIT8LeWIG3dgmjzvV8oGPUyMwdFcsnwaOwulZWHStmaXYminlOFvr8XaG4XDY0uONxgp8jhYl54EHKHh6ve1tjajbaszJON8e9/dz2hrxiC9etJuGk+5sOZ8Ll3M7+1pp6jX6zgop/dguysJHDrOkif6nVszIwZHPvoI6LGjiVsmPfMDkVVcdTXM2jEsNZqpx3xku56SexI/nrw63bDVCGQzwHLR19QFXenZUazmarCfCISBnYb7Kq6XaguJ4bgqJ7vNCnJk4108AvYt+7/s3fW8XFV6Rv/3rHIxD1N2qZN27RNUk1dUiiU4tLFFttiCyyLLwvssiy2uC/uXqhDKaXUUndLXZK0cc/Exu/7++PGJplUoAL7m4dPPsycc95zzpXOPfc9z/u8UNlA6D4Dyr69Wog0aONOmwZZWYy4divsfcBzCygxEZ3DSdKU62D3Pq1u0iTN02a3c/ZdUyF3f0u5DgieNRPjoAHYhgzAdLiEssgojAF+mLvE0zBrFuUTziRx9izq+vRhz0P/JHLZEkJffY3Yq65CGTAApaGBsIkXwlXn/yIJ/N8SUuKCSYkLptbmZNneMlSBjO7hhJtNp3tq/y/gW3z44MMR0D8ogMNWB5VOF9GmNsJRSUnkPPMSriceZf6L72AuLeYPd17N4WUzAQitKSJvfg4AhtIK4n/+CWdqb0w5+ZQ/fBX67fNbutIXlhI8KIUd/7qB5L0qkV40LXYWVuH62z8ZO2ms5mE4CgEzok8fIpqFqjrB1hWrGXxg/7HxVtqEu85pyOOsLgM9ql2qinqaFx+/xI1rraslLLZdGEsT2TVgVzZOuw3jUc510crZJI656PgGXvU6OBqg15nQfQo0vIXNFI8aG03580/yc6gTMpKIMzRwkaUGq92EO9wf10t/pz5uNwCGzQcJrrNQU2dFgkOpfOo51rqDiNy5jTNqLCjhZlwhgTie/SfVfhofR19cjjEpitX/vIngzw8yescKfvrrk1w4421M99xDcG0t3Hcvkd9+y5gLJsMFk1EHDMCSns5Pt99CYGUNY576Dzz7L+04foUE/m8FIf5GJvbTCKqbDlVTfchJfKg/aQlHV8f14ZfDt/jwwYejYHJ0KEsqa4k2OUkPbpWontg1EkIDuXtgN9hRS63RyIAJXkJV8/Lg7I3omzwIkYs/ImBAG9GykDwI/YGA7n15wxnM7S+/xdyJV2ILCyc12kxJfjE1eQWMueY4H3BHQF1dHeJ2o5sw4ZiFvJo9ADkXpnNJN8/Ed05R4Xfo+Sg+sJfkjBFe61x2+zH14awqwC8y4ZjHrMhejKusHDWyN+TuxbhtLxEhZuY/8A9umHAGoSkp9Oo1UttGcYRATCz73/iIhN790aek4JdwrVZXuxI14ht0n75MUEI6QSkpdB+eAsEqxMbgeuMhDKkXYEhJITDtfM1m5Upk9yNM+PdbuF1mXG6VjKF9YOSLrV6Mak2srhm6yy8neNpX2AZFMqwsGL/IYH7c8AVnh49ofYD8irwwpwxH8dQoikJGkpagrqjGyqJdpRpBtWck/kYfQfVEw7f48MGHY8CZkSEs3pDPOqWqpcxUVEza4qXYBg0ncN9Oyp/4O15jLJoiFppzZpjK8jrmimlooN/gC3mobzr+DTXc+tXr8NFHNNgcbPjoWzJvv+aEHs/mn5cxZvwYmP+9VuBNyMtLuOteSwkJVYc6digqfjpwqS6Upv9Oh/6DqqrHNW5Zbg49Bw/zKKsuLqSi4DBhsfEY/fyP3olfMKrLgc5wdHd92bafUZ124i59srVQtxL8PsUWlerJ3Wi/zVNTA1FRnnWKgk7Xrm7s2CPaKNHRlH83G2fOflJuepjAmV9qCQczM/n+xbsxFVcw/LZHWJ63CoCAM/qR+Uoxl117HyG7D8OsWYxLH8uSH17lrGXL0J2IiK9ThWP01HQJC6BLWAB2l5t1OVU4XCopccF0jegkP44Pxw3f4sMHH44Ad60dFAV3rYPxPaIwRrUJo8xzwcQzMDZ5DrrP+67zjqKjW3NmDE/3fIAkJcHbb1P752txz1iEv6UcLr0U1eFg/YffMO76S08YmTPvYA75u/ZijgjHYGj3z/8YBNF+KNjEPf3O7VAerJawsyqHBY4qVBHkGFQ32gphHivkCO0tpgayN3yAQVEIDutJQvI49PojLwi6pw9i1/IlBASHNJFmdRiMRnoPG3XMc+oy+kLyl3xN90k3HLFdae4uFHsdccMv86xITITGRm65YLjmPejVCwYNaq2z2xl8132Qm9fK62iqUxxOwm++Hw4e7sD5iLjvRTj0QAcbRo4kvySHzLgBrQTWvDwAVHScE9wbjGYuTmoORc5DzphI0UtnEbImDJYuJWjSJM4ecjUVg+dR8O6rDF63/+ies98SjtFT42fQM76PlkNmd3Eti3aVEhJgJKN7uFd1Wx+OHb7Fhw8+HAHWnZXoAgzoQ/wwJQR13vDqq9E9/m+v2W+bf/ABqKnBHmzk4PlJkD2tpYmxsJxQWzVL124jpiCXobUNzH7qb0QP6cXy1V/T9nEuCCHmcEaMveK4j6dw7wHGXdhm8XAcQl42lxM9Oq+eBUXsjItNpmfUMC+Wpx6VJTvZs+lzVNVBoDmeHqkXeZ13fO8U4nv/ioy7gF94HOJ20lh8kMD45E7b6d0u9uc0crBuC0DLFfUrKaJnr36UucFy3nlE7thKxD33sfSdKCKztzC2xkJNeBTGkGDqn3ma3UZAgZDt2QysrcVuMUNgAI7H/kWFpRpjjYXwPn2ouHIQ3WwmmPEB9DKBIRDKa2DZUobuz0YOFsLMma2Luawsxl6+D3bndPBiKIqiSfC28ZApikJ0aBe2W3KQq65Caes5O1Yy6qkmrR5HssL26BcfQr/4ECyNTpbuLUMEhvWIIDTgFyQR9AHkNwaLxSKAWCyW0z0VH3wQEZHGnRWiOlytBbm5IlFRIiNGiISHi/z4o4iIOO7+q8gzz7TWZ2aKZGSIvPiiiKKIhISI6HRSfvNVHQdpthkzRsRsloNfvC2rd60Wl9slbtUtbtUtqqq2NP95wbu/6FhW/vDTL7ITEXl/7xIpafT+73JX0RI5VLH5F/d9MlFddkC2rXpD9m+f6XEOTyRUt1sO/fyZ5M7/UFS323sbl1NKFs7oWJGbKzJ5slSMm6h9T00VGThQ+7xihUhCgsxculEkP18kMFCkoqKlzhUXJ9sXrRF3bq6ogYFiy80R6/r14jzjDHEHBYqMyhAJ8Bd57VGR3T+ILHhbZOIw2f3VFHGaA6QxPlos6b1l5wv3SuFZA+T77FdEvvpK5KGHOtznxR/foY375pvafb5ihYjJJLUZ/cUxMF3kL3/RypuPacoU7XN+vvbvoO3xNv/7GDBAu+eb26Wne/7bafq3dULQdk7Nx/gr4HarsuZghSzcWSL7S+t+/fz+B3A8z2+fzocPPhwFpu4hOIraCRJlZsLatbB9Ozz6KKIK1rPOovGG67GWleEaMQLrN9Owvvcu6jvvILGx2A7sx/H0U4TN6kTMKzMTVq5EffddrPPnM6rfKPQ6PTpFh07RnXYdjTpnI7EB3pVD3eJE/0vSyJ8ChEUnM2D0nUTEpLJ99WuUF2074WMoOh3dzrqO+BHnsn/ma1Tv7igkp+gN4HZ06u4P2b5Jeyvfvx+atViKi6GoiHH33Kp5BMxmTy0PAdxOdPX1KJGR+E37Bv/oaAx+fqj9e8PqDfCPf8LzH0Hf8yBlMoR1Iza8P4bJ5xGQ0J2QL2fS/7NFdAntTf9AWjVGqqs97vPwl+dpY95xh7ZVk5gIF16Ia9qTHPjr2bBunfctO29bHM26N++8A7t2ebZro4njVXvmROAY9VuOBJ1OI6Oe3T8WRcGnGXKc8C0+fPDhKNCbjbirbd4rm34wxaVSXBtLQ3ExjaUluK1W6vLzady7D9XhQHW7seTlUpvUHaXBdsQfvXl9gumz6/Cv+mHsDDXOjpoWx4JVZQcYGNF5Pg9VdaJTfhuLD9XloCDrbQqWvcHhRS/SWLwXgIjYFAaOuYeqkt3kH1hyUsb2i4inz+X34qivIe/HjzrUm1PHUL1llWdhE+enOm2Qppj7yScQG6vVxceD0YjidkNODoSHtz7gExNRnA563nuHtq33wQdaXVN/Lbj4YoiLwyumToUPP2xZHCjN17BZY6QZR+BIKOIgKjqVRkslO1fMZc+2texY8jWuRQup79EDd8Ywdp59Eau//p6tP63AZm0TRVRbC3V1rUJ2f/vbMY15QtDMcToBSI4O4uz+saQmhLBsbxmLdpVisTpPSN//szgFnpjjgm/bxYffGmyHLOIoa2gtaLdFIj/9JO4Gh+Qty/de/9JLIiZTy/eii69sdU+37XPKFNlbtFcWbFrQ6truBKd62+WVHT8csX7L4TlSWnvwF/V9ItFQuFtyFzwljjbbQ4d+fqFDu5ydP8j+7TNP6lxq83bK/lmvd9iG2fjBv2XFD8tk1aJNLX/bX/lAXAZD63ZD797a9krbbZfnnhOJi2vtKDdX7OdfIFsXruy4jZCbK2pwoMc92hZVPz7a4R4WEcnJ+a9HH23bFH/6F8/yESNEDAZxJseL2tSHtbFeqotzRQ4eFJkyRWq/ek3UL79smVtlcblsev1DcQSHiDpqlLaNNHas1u9XX4ncdpvXef3e8P91S+Z4nt8+wqkPPnQC1WajYfVqXNVAm7d6XVkxAb1SabjvSYwrFqF/5zOstf4U5TfiDvMjYMsO4iy12O2CITwK17yfUCaeS+PHX+L/3QzcS5Z1dE8nJXHwjefIztvKlFFTYAi/KShHiUkRcaEop+fnpHb3euort6G6bOgM/iSd8w+Pep3R3MGmR//zKMpbw57NX9B3yLUnZV7B3fujDwhi34yX6TPlHm3bBRj8p3+Qv/AtolPPIzCul9bYr5GG0GAWPXsv/mUVjLv1YfIevZeKgX0ZU1bKoDuuwpFXRENULNMXbcXYWM/V116AqtfRY9t1FF/9J8JnzKSk2wB0NhsJj94Fig5naTGHzhhP2KfvUGKqw1RSRu/bHsLQJQynw8qua84idOxYYt57C/8rryCuezROxxs03HYjrqRuBA1Ipfa5J/D7aRH2BT+yfOTbBBRW03NodwoeuYLujzkwlhYR+ObrsHQp/pMm4R9gbome2Vt3mOrufgyf+TVbh0TjCAnEP6SSkv5d2X/DnQwt2Efo9CYPy6hRcN11msaI3Q533tkaQfM7U1Nt3pIBOFhez8+7SgnyMzC8RwR6X5QM4It28cEHr3BVVtK4YSPBE89EMbbbTsjLg0XfYrpsHFw2TksWl9kP49wdBAab0PvrUSMiqfhiHkpBAfHnD6dh+DhKcmpR+55Ftxee9xoVs2H/Bq5IHN4q2X6UH1nb3n048w+j+PnjGbjauas6IsCPpZW1HcoFcIlg1usYERqEoc0P5LryHPqHd54vBcCtutCfrsVH6TriR92EqroweuWkqF7tuiSNQtHp2bdtOn0GXn7iJtTmQRnY0EDPhx9kzzcvkZJxEbox49GlptKtoYGqq0uoGpRMo92f2i3VDK6p5ay7/4O5oQFCwuj/1mcwfTq43PgFBWKKi8UUHs6tGxdouhxjRuEMCGTz1IcY+8Qd8MA9JFkOaXULx4LZzJZbHyHl3hsJDjQSmz4BkurgrMUEv/gi3HEHAz/6gU1PfE3S/Mtg3HgCvvsOCgoIu/RSbezwGfhnjIeM8Rj6Tad75BU4DVUogesxxZ6BnzIbqxhQ/ng1jB7vqRMDWAd35+xhf4Z73GQerIWH7oO8PMo/nMOY6y9m3ycz6HP4MIu+nIXRZmVc9+74BwRouYYuvVTr5IEHtP//TtVUk6ODSI4OapFxF4ERPSMI9v9tbFOeLvgWHz744AXicmOIjuq48PCGqVOR997D1TeTtWaVqEAYa9CRmBIOzkKICCe4uph+8XYYNgyrKRAGDtT26Jt+QCvqKmiwNiDNhLuj/MiK3Yaropzgs846ruPq1/TXGepdbrKq63CLkBzoR3KgP4sK1/PwgCOH9bpVB0b9MQhynQSE9hhL2eZviR/1p05adE5ti+82HIfVwqG9C+mecoI0KgoKNC4DgMOB8aF/0HvbVnLef4KeY0ahm/MdSkEBkZdeCpfdimRk4DL7o3O7cZQcwlRuoT4mmi/2rSHeWcXkmEgee/l27g/pT59Rk8k6dxATVBWd2Yx12jfopy/TuBHNodJNXgeAqrxSggMMrfyGq1qTAeJ0gghuPz1cfrmWawY65VpUnH8u5vffx3jVVbBiFcHX3gpbtrDv1dsJ1Rk8dWKaBOqU9W9oxu3CuA16A3X1laTffgPF6xZzfv4+1CuuoOGzruRaLARsWEp4fQ0NV1xOZPt5/x7UVL2gWcbdrQrrciupt7lIiQume2RHz9z/B/gIpz744AU6sxlXRSeEz6YfVldlJQ3r1tMwZAi1I0ZRXVCLZX8BUW4DqtXaSqL75BPIzgZAxo9n+2c/aBEETUz+6vpqflz/I9dPvB69vo2Mcyc/sg63A3dKD4LGjOlQ92sRZNAzMTKESVGhCLCwwgLixubuhHDbBJfqxKT3O+HzORYEdx+Mf3gSRas/+UX23VPOxm6zUHxo3YmbVLOo3EsvQWEhhoBAep53M/XFuTgbajyurTJhAsYvp6HExeEXFIzx348Tbnfy1z6j+EO3VMwGPy5ITMdeWY47PJRhc1cx59Aa7EsX4Ro1nGE3XdQx8+zGjVBSwhl3/0Gra45QAU3r4tprYc2aVrvrroOSEo8Mxi0Kt3l5EB1NxJKlMHOmZp+ZqXlGAgPp/dSXyMjR0LNn6xhNNum3v655gRZ4RngFb91N0IVTYNgwGkOC2ZUYz8G5swjYspm4mmJir70N9zMvUXPJJLaN7IPTbu2QYbkFTWMxYYLXsX5r0OsURidHMSk1riWz7qZDVdqLx/8j+DwfPvjgBbU/zif0wgs7rW9Yvx77nj04S8vQBQUROHQIw8/sjs5oQmevpbhbf1b/6xO6Lv2e0I9msVNJJqC4gAGlDWTlFlFxaA8jaxv4YM5SEkOLuTLzSoyGJi/LEYSQRITFhxdzVu+T73LuFehPr0B/zoi4gi2lW2h0NRLhH0F6VHqHsF+36jxtng+A8L4TsK7+GLulBL/QTiI7joA+Ay9n1/oP8Q+MIDy696+fUHm5dg03b0YSEyk+lIdSVEhcTjH2EcMx5hW1XFvV2oBUl6OrKMdYVYnjX4+hT0xEX1mpRbTY7Yy7/HYim8S/TJMmcVleHuqEidS8/i/2PvEZXT/+msP6KEATLUvqN5DV/3yQurlz6fvl+6wLdYKiEFxYwqj0Pqx/8GH6LltHyEefUX/WzZSW2ghNH4r1lfcwL1mIbu4CZPBYFD89OrsbZfx49t5/DxndesD550PvpnM0aRLOnWsxfPA++ptv8fTSZWaS/WAmo+MuRi69hNKh/aipr8Kdc4Dg1G4U/+l+Yp0Gurz4Tw6PuRKdwR9rvyEUX3kRfeLjMa9bQ/Ilz9BQX8v+eV/RpW8P9PN/JHjevI5qqt68hb8DnkhzZt3KejuLd5eh1yuM+n+SS8a3+PDBh3Yof/NNbLt2E3rRRTjLylDr61GtVowxWsr0xo0bCRg6FPPw4QDYc3NxV1cTmd6kbunvxuqv4/JJyTDpHhg2jH5DwqBOD/dv4sFPnoCtW3HPmI7Oz8k1Z/zRcwJe0tg3Y0XhCkbGj8SoO3X7xUadkeHx2rFWWitZXrAcQUiNTCU6MLqplYpOd3p/MKMHXERF9jziRx1Z5rwz9B9+E1tWvEhQ2F8wGgOObtCM9g+5SZMQhwP3li3orVZq/ExUlZUSfPAgao0Fm59g1AvOO6dSNySJiCUbUVevQudyYw8OA39/rDo/qp94iarBwxhuqQW1ixZ+++GH2v1QUIBu3jy6lhbRpbYGf5ubxP++2JQ8rhHWrWDEw0XE6MxgsTDk5ZGtyqMR89mpK6P/1GswpaWRdOgg1NYiZjO5GzbS/4Ip6C99E3v2ISQgFDlcin+FlZ9/slMW/TODquqwVpazetMaRleWk5dxIeOfeAKlthbHssUgoJSWYCgvY++Oahp3fcrwxhr2F+4iODCUsIBgQo1RuAYPI8nQgC48iJQrx2thxg+swWirBpMJLBZ44AHMkZH0T0xD1m/DNnQM9Qro6us4dPa1JAxJ5tCaAnrU2dm75xBVa9ZwdlsPwu+EJxIZ5MdZ/WNxuFTW5lRid6mkJ4QSF3r6FvQnHSc79OZ44Qu19eF0Q3U6xW21SvXMWdKwaZPY8wvEWVYm1p07pWHjxo7tHQ6x/LjAIwTRYQ5qVWdsDpttp7C4/Mo/yMGKcs/O2rYR0cIuKyrE5rLJgtwFUlRXdDIO+bihqqrsqNghyw4vk3VF62Tpnv8e3egUIH/pGx3Llr11zPZ2e71sWfHK8Q3a5ppVLlgp9uh4cRlNWt0bb4gYDC1hs/boCNnx6TVS9O1T4vY3iXPNCs32229FRRG1a1exJvUV8fPzCLUt2jbPU+G0qbykZI/kr5kn0q1ba2h2U920ddM0m7Z1TXP9/udPtM+DBml1q1eLhIfLnnnLtHZtQ72b7uvqfkPEGRAoNTPmepRXpQ3tGBbbVNfQN81rnTMsXOxDh3nWNc07b+HPHefdVGddskgkP1/Ubt3E8vd/ycaVh8Wyebc4gsOkdshQcQcGStazr2lKtm3/LWVniwwdenzX9TRje36NLNxZIoXVjad7KscMn8KpDz78CigGAzp/f8Iuu5TAIUMwJSZgiI7Gv39/AocO7dC+duFCgs88Q/vSpAi56pUPW9UZ2+63o22dPBsbyeDde+l5tLRqU6dS/cbzrCxYycRuE4kPij9Rh/mroCgKqZGpZHbNJC0qDZsSREXFEhob80731H4VTCYzXXufxZ7N3xyzTc3BQuoKytm7YBbVi75FHCotu1ITJoCiwGuvtbRPvf4L4vpehBIVQ/2Lf0dUt1ZhNKLExuL/0tMaH6S9AFbbDLVN+YIcDit+VkdrgjjokEvIo64lU3HTBJOTtbrgYOjZ5m5sd8+Smcm+/87A8OEHhG5c41G+8bVvtTktXeo538xMyl7+0Guddchwqr6d41nXPG/xMu92x6RERxPy7OMMHdOVkHB/7IOHYV6/Ht0HH9Avbz9fLFitte2MJ/I7QHpiKGf3j2VvSd3pnspJgW/x4YMPvwI1838kYOhQaBcVY62p65SR/92u7Uzq25OgP9/W8QHTJo29iLD64kEU3Xk9E7tPxKD7be6SBhoDmdznBqKizkTERUXFEiorl+N2W0/LfFS1fWjt8RH5ImPT8Avwp+jg+iO2c7vd7Hszi6pNxQTszSbhjgdJeuNdcs+ajIjgHDVae+j9+c8QGAiJieicThwjhqKMGoXy4YcEPfku1oocTclUBLW4FLnySiSxG67b78EVFoPY7YRfdy8yciTud97B/bcHNXJiVhZRl95A5AXXdnywZmVx5i2PeX3oOtyO1nup3cP5qGeqM1nyI8mVe6kTVVDdHeskK4uYe+5GRo7A/dD9uMvycRXn4SrMRZYuwXjnXV6PSVEUXE4XXH010RvXcmZSLItm/YTbYgGDAWJitC0rH34z+G3+mvngw+8AJQf2UV1XjbJ0EaIKxrIykhYsQG8wcI6qaqS8BQta95mTklj0n2coqDjMRf0HHDGLbJ2jjpWFKxkVP4ow/7BTc0AnAGZzL8zmXqiqg5qaDaiqHT+/OIKC+p2S3DTmuFTqD20mpEfGr+qnR7+LyV7/JiGRPQkKi+pQv+mT6TToA+g7eTgVu1zU9k8hdO8+1G5d6b3se4pT+7Lxk88JmTWTqAP7yRk0jIsGD0av6NFv2gZdusANN2D4+GPK7rgEv3Mno3O7oKICUHD17I99dxW6LTsJsNRSFZVIWFgINS8/x+ZoP8K2ZDPKUkOxI56IsHBqXnqFlfGaFou5sJAhg4bw0f1TuWLDVoK+/JCcUG1B5l9UytAb72JUXAI2qw01Oo78h58mOGsxfu9/ivXMzvVOKusd2oeLLoJu3SA9HfbtI3b4CirSY4lqG2rbHlOnwvPPw0cfQXIyAbt3s/Lr79l49nkkZV6C4Z8vUHj2RaSlD2f1eVO4oHQDum+/whWigF6HrqIaJb0vlhv+RoS/rQMXSmfQYatvxORvgqlTSZg7g/jRo7GHhjHz7//h7IRQYkcN9551+jcOP6MOm9P9v0dCPclbQMcNH+fDh98D3G6XbFkwz7OwKTupTJ4s9oBAkfh4bY++ifsxd8dWmb599VH73lO5R5bnLz9pGVhPNazWQikrXyTl5YvF4ag5qWO5XU7JX+bJP8lf9uYv6svlssuWFS93KM/Zuk+2/LSm5fveZUukLnN8K7+gb1+xBgW1GmRkiG39eikZN17yRo2WHYOHSHW/FKnp3VNqeifLz3O/lJIBaWINjxbrig0iPXtqMuptuB0LFx3wyvn4fE+RFO/Z75nttonrsGBbYcv47etEpIXn0XaetbO2dno+1i3O6dhHfr5Y0wfL3j3lXm3y5q9r/dLGLmfzZqlNS/ds3JQltyYt3VNivk2do/+gjnW5ueIOjxD7iFGemXCbztGBwgr58d3PRbp2PWLKgt8q6mxOWZdTebqncUzwcT588OEko2jfHpIGdqKBbrWidzo1tr6qwl138c22tegU4Q/pozrts8JaweJDizHqjIxLHHfas9ieKPj7dyE6aiKRkRNoaNhPRcUSLJYtJ2Usnd6bM/eXnUe93kREzGDKCjZ6lBfvz2PQJI1/sHjNWvILSgnwaxOVMHEieqej9fvUqfjNmEGsOZDuoSFEBgShGPwJveJqguobGdg9ndjwSHQ6Hf5h/lryOJMJXn21zSEonpyPJlhdKuaGOu1tvrMkaZ0lUKut9dwanDoV4/yvj/HsNKGmBgXpVPOrs22cCKcTdzPXpS2io9nx7AuoM2dqEutt5x0dTf0Tr2vZbtvVqaPHUvrO556ZcBMTsdc3EH7uWZxz322tifd+ZwjyM9Bg/2UJIX/L8G27+ODDL0BDdRWJfVM7VjQ2wooV6ETA5YTAQFx5eUQGhHFWn74dmquisq18G7X2WiL8Iziz25n/M4uO9lAUHSZTJA5HJf7+XU7iSO3eqRSFgqw3iR405YgaIKrbRcmajzH4hxKToSm6duszgR3r3yMmUdvGEREctlZOicPpZPIfr0QZM1ILt23SZ5n/1vNc3Nzojjtaw3GTkojbuJGaXn1wvPwKs+5/FOfGXVy+aTM6RafxGWbNggMHtMVBYiLY7WTcfQXk7tHqmnOd2O2cf+0lBO3f3VoOrbyh7UWt47dFZzoyd9yBY8lhjim4s00fNe98hrsDz+bIdiFbt7L0mec4s21dE6lUbzTgrqhEd6wkWkBvMuCstYHB6rGgUiZMYNbUu7i5sUTbqunT57Rpf6iq4BbBqPf+zm93uXG6VHQi6ERFZzRg0Ov5H/058C0+fPDheLFn9XJie/bqWJGUxKHrx5OwdSP6OiuSlAS5uehMJq8Lj+zybKrt1QyIGvC74nX8Ejid1VRXrycwMImYmHNO6diJ42/D7bBSvmk6Tms1OoM/MUMuxxgUAYDLVk/F1tk46kuJHXY91tK9lG2aQczQPwBgKXOyfu4yAAoLiwiNjmHNwu1aXW0t3274lsCiMgYNSGbrX69k8jWbmPDgk9Q8/w77bryFihEjCSguJrVnL0J370QJDMDPUsG+ay5iUM4y1G4lmOxWCntFE2kR6p5/ip1PPwwohP40g8GWGqrDEzAGR1D19GusI5mAkgIG9h3Ki7c9w/UHVhHyxVz2BLcRzlegtqCOA8VONOEN7QlmKCkgqu8wSv75NkFL5+L34RwqJaXFOdRwsIogLCg6XQfFzZJ8C7OWVRFYUkR6/4Gsf/J5Ehf9RMDcOZQGxGOsKmODXwCRfq0aKZVWF2s2bALAXFTE4AEDWPHs83T/aQGych3fjGlZohFYVMzZi5fSY/sODIdzsd53E+5v3gAEpaySwIULMG3egLuknINPP0Xd6pUAmIqL6bt0MWG7N+E+VMrhh24lMTwUR0wEgfvyuHLjOtTiEsRgpPHzr3FFxbH+rsfwqyxn8J33sPb5t4/rfpKm0ykuN4rh+HgYThWMnew3iECgUYcooKLD7XajqoIAlSUVqN0vQBdwHPozv3Wc9E2g44SP8+HDbx0HN63vUFZdvl/KizfIvrfvEgkPF7eiSHGvZFFBJDTUaz9Z+VkneaanH263Q8rLF0tV1ZpTxmFpz/loD6e1TgpXfiD5S9+Q/KVvSOGK96SxLMejTe7CZ1s+79r0XcvnNR98r2m5ZGZ68gua+QxN/7f27CmyfbvWRkRk2yqRyZPFOWGc2CaOERkwQNPYyMgQ19zZIgkJ8sOutzx5HSItvIUfsrI6cj5MJqlNHyGqFw6E1zk292cytda1tROR1Qu3H/U6qTk5ol52mbhcLnE6neIeOlQcxcVis9nkxbWbpbS+oaNRbq7GL4mKaplTZf8BHmM3n7uSRTNFvvxS5KGHOtTtWPOayFdfea3L2vOmVnfbbS3aKaI900XV60TV60TOO6+V5/I70v5w19dLw/qOvzu/NRzP89vn+fDBh+NEREJX9q9fTXX9fMxBPVAAh6OB0LBUEhLHoNb+F0WEuJxc0OnA4ejAsq+wVhDp//ti3R8PRIQay0ZcrloiI8ai0/2yvC/ZeZsorNiDyRgEaFsjA3uOJTos9ghWR6ayGfyD6DLmJq91jcV7qdj9A6FdR7SUtX3/Nxn1nqqZ55+v5UVJToZ9+7RtBcAyYQL+H37YugWwZz40lIHDiRIUAhdeqOX8eeQR9PO1XCQKTbyOZv5Ge35C27qxYyE6mr3Pfk3GACOMGdNq0y6xHXfdpc2tGc15ZwoKPO2acLRtP0VRQFFa8xDdeCO6Tz5h7qWX42/QE+HfybUeMQLMZo3Lcuml7D/3ckZ4OU69vRr+eJO2PdIuS66gauG5L7/coQ5F0eoefxxyc1H37tHmKoLq74euwYp7wzr0dXWdpi/4rUJnNqM2Np7uaZxQ+BYfPvhwnAiLjSMsNo7qah1ut5XIyAkoisL6+X2pKwmhW98Ywg7WaGGF06ZpP/SvvqrtLzdhV+UuxiaMPV2HcFLR0HCAhsaDhIUOxWTqGKZ6rFi2YwVmE0zOuKalTFVVFm+dRXL8QHrGn4AcLE04vOQVYgdfSWP5AUKTxhDaU1t8WKqLCTS3HoOItnevg9bkcG0XI+edB8XFhERGat9nzdJIx6ECt01Af8d7SI/u2v3Qlr8xcyYjrn0R9j7gyd9o4nyMvPNOyMnxtBk5EofNDTUNHTgQhIfDzp1aSO/Bg60h30fhTniFtxwpzQnn2pRPSk3lLv9wLHY7kYGBHfsxmzW7HTtAhLw//okRQxI7NFP9wrUPXkJ3leaFpZc61eXE7XahnHsuyhtv4LRaMImgAPoGK2I0oiuvxNGzH4ZFi9HNntUxR8yJwO8gp8xvAb7Fhw8+/EKEh4+ksTGXwsKFWCz1rFFT6FlVQGpEMtTktb6hBQfjjTWmU/63gs2czlqqq1djNvciJvrX8zpErWFYH8/kfjqdjrOH/IGtOetYvGUWer0fw1POJNDvl++F1xfuwuAXTOmmrwCIT74ZANXtZvuKWQyf9KeWtv0zeuL66xKc/QcTkLuXgr9fR8iqrRzOnkbAwTK62upwDU1j05B+9NCb8P/0bYoNNejdSejsNRi796T0hcuJ2eMk8LvPONTfH7/iYpKG9eeDvw3l8vWhmL/9kvLuURh0BkzFJcRkDGbBlX9jUnkuphnzqe8xHGNJLZFLl5G640qkYC+NX3yJrtGOPj8f4/nnQ12d5nWrrYVu3eHRR1GaH4BHSFzYadhKZzlSmsolPx/3+RdwzhdfExkYiIjgFnCpKm5VUO1OzMuycI8ajT57OxUff4nqbjdWE1FW9/UHSHQ0SvPDe9AgbbwZM2Dt61rbZhJvdHTLQ9760SqWnVdPSnchPjIM5e33UCZfoC3+AEVVISoKXUMdrvIaTJ15UE4ETkJOGcXPH9VmQ+f/v5Hvxbf48MGHX4HAwB7k5ZXR0KhiOZTGHL9q+tgLiR/cH4OzkcCpU2HhQk3hsgn1jnrMRvNpnPWJhYhQXa3JWUdHn3PConViwxLZeXgbqd0Gdqgb1OSZaLQ3sHHfYpwuB+EhXRnc8/jFxQLj+1J/eCM6YyCOhjKM5jCcDhsbF76Ju7Y7RlPrwibA7AeTJpJ30wv0qVlL5OwvMG/NIfT2N2DrNhyv/Ae/b2cw6u1P0fUfiH7LVsxnTsZ96cXo1q4jcPduwv85izAnKLW1hP7jKZTIKPxVP+697BP0af3RbdqM/8hRNFxwHvodewhasozzD1UToHej1Fqoue4OXLVOGgaNZNafbmHk4WwCZs9kf1wkwVu3MryhAQUQVUVxu3GXVaArKqLg4RdwdOlGTM/+zL7hYVLPPqARVUP6EVCcz7gbL6R7QhyN2Cj5y+XUj9HOu7GwnHhLPh/M+op+NSZG1jbw09KtmEuKGFxew4/zNhC2Zy/jHG6CCeOn7UX4F+Yz6przsCanYLA2UvzHG4kaOpKc1z8kev4cApcuZeDFA7xeD1NoJO7hwzH88MPRH95tHvIjzpqA88Enqd70FkqlBcPkC1sWHvj5aeG5Q4agX7eOinfmEvf0rV49KCcUzd6xEwD/tDRsO3YQmPHrBPR+MzjZBJTjhY9w6sPvGVanVXaU75CyhjLZULxBVhSs8PhbU7RGvjvwnTjcjtM91RMCq7VISkt/FIej+qT0v2z797KvYNcxtT1cdkB+3jRTsr54RAr3H5KiA4dk84Llsuqr72T11/Nk9dfz5KeXPpD66toOtm63W/bM/KsULn9XrA01svr7F8Vhr5ea0krZtKCNMFwTuXHfj7nidrvF0TtJ5IILtLpmsuPkyVJ2zmCt7KmnRBIStM8rVog9PlZ+LtrpmTitSZyu+KzzvNpIQoLsX5Db0WbKFJm1ar7WrllIbMUKjdQZGyvOAL8WwqW8847WJjdX1Msuk5U//ORp19TfopwftXGaibLNxxwVJZUD2yWJayq3DRsproDAjonl2giRyYABHRImHp6xxet1rPlhljjPbzoXTaRQ1e0Wt8st21a9LHU2q1Q3NkjFzmxpvOhCOVxdLodXLJXKlJ4iIvLNjVOlMThUKlOGitvfLKreIKqfv6iRkSJms1SPv0KqR18lbqvN6/i/Gs2E3zFjOibV+5Woy/ptk9RPmcjYs88+i6Io3HPPPS1lEyZo+99t/2677bZft0LywYffCfwN/qRGaanmM+IyGJsw1uNvaMxQRsSPwKgzHr2z3zhstiIaGvYTEzMZozHspIyRmX4B1Q3lrN2z9Khtu0Ync9aQyxh79ZNU5RdTXVRG0uD+jLzyfEZdpf1FDkrDXV/fwVan0xHT/1KsjTVsXfYRI869C6PJTGhMBI2WWlR3q46FqILT6kJUNw1nZWjuf9C22TZu9Mw7cvHFENekLZKYSP3QweibiaXNfIukJHj77U5tZORIbFZXR45GQwPnnX+VlryurEzbPgCtXVUVepsDgoI0YbJ33tGWIQUFMG8e6Q8/qPESRozwnsCu/dt6ZiarPnitY5K4zEzKZy6g+PGXOiaW89Zf81aJxULUrRNZ9fKTzFo9n1mr5zN79Y/MWj2fNfWlqKuysAxKxj18KHPO7U/28tnsXDWHClcXvt2xnO/2rGFlXjbKiuUEnHkm8ZPOY/PVZ1NYuJmlKWezp0c/6nXgjIkFVcUVFU/NoMlgMBCQFIHt0uvRdUaMPRHIzISVK70n3Ps1OEFelN8CfvG2y4YNG3j33XcZMKCj6+yWW27hiSeeaPke6I185IMP/w9h1BuJCYw53dP41dAWHgeIjBx/wvpcX7SBamsRRp0fguAWFyF+kYzsM56c4n38vO4jYi21uImiS/9RxCYme+1Hp9ORdsYIr3XVFifFS7dTFpQDaFsTSHNEi4GKHIWRD9yNTtf6XjZg4gi2LlzLkHNHQ1ISOXe8TK/RXdAbjDScM46gwgbEWQ0Y0F1/DcpXXxG6fr9XXkXQ6nUMufg62LWnA98iYuNarzZKVhbJu6+GQ7tby5sWLBVTryVh6TIoKKBszDAOpycxVFFQzGaw1GjRLjExsG0bfPstJCbimnwu++77GxnJ3bUtjf/+V1sUZGUx9PLdsOdQ51EgbXkSTXCrQuP5l8EN53vyJ9pzS5qJsk1bJfvmzmTMU8/CfY96jpGXBxMXYZoxA77+mm4LZjFgwpSOcwnPgzPPwr+pXe9FM6mtzWVUVA5hdcV0Ka9E36cPSgEYJ01Ad9Ud1H8dgSnIhCHCn6rpywhM74l/327ej/VE4ATzShQ/v/8Z3scvWnzU19dzzTXX8P777/PUU091qA8MDCSueeXugw8+/E+h2eMRGZl5wvosb6yg0l7NuckXe5Qvzp0PQM/4PiQGKNiqsgnpeRl7Ny6haNca+o29BP/AoKMP0BSBMCi+JxFGF/LoY8g5k+DwYfRjx0D//tDQiPvcKR4LD4CQyDCs9XWobhWdXoe4BVOg5rmKv+AvbD9gJi2mB47kJBRrIzvGDyAquSfr/3U3yWvXEvLFe+wOsRK+bSdjauuoa3SiC4ug+sU3WBPVH4DILZs5o76O/AobkSER1P7nNbINvQjN3kBGjQVLqA0lKISGJ/7DAasRUDCVlhDSUM8F85+gb64/d6s68pa46RcQiMHtRG8woHc4qL/gCozbNqH/1xM03H4fpqIy8t1+ZLT3cGRmsumFm5m4trpDFEiD3Uatza2dxz17NK9JcDAEB7NxbRHd0FE24mJ0f3uemivvwD97A11qLLiqyjCEmJFnHsdtaoCScgzlRRz89p+EBWYc9U3+rZ7BXLN5F2zZos2nbQRJ31bhvtu6R/P4ms1kN/6JoDPP5kVLOP9dtQSlaWHC9u2ETspAzh5K49b96A+WAFD5+Rri/xmLLuAkekFOIK/EPy0N286dBA4degImdprxS/Z1rr/+ernnnntERCQzM1PuvvvulrrMzEyJioqSyMhISU1NlYceekgaGryIzjTBZrOJxWJp+cvPz/dxPnzw4TcKq7VIyiuWnvB+l+X9LHk1hzqU/5wzTzYWr5eNxetl0/7pUrH7i5Y6p8MuWxd/IztWfi+q233kAZo4CN8tOODJaWjDTbDvPyj1vVO8mufvzpWcLftERCRnyWGxbt3TIuRlS+qr7e+LaH337SuqySTVA4Z4Cnk18TdWLtrkVUzMHd9FvsvK9ZpAbu68xR1tmrgFFX2TxBXgL3967EV555FXxB0To/FSvp4n4u+vJakT0eayebNIVJRUDRrWkb/RzPlobttGAGxf4QHZeXh3Ry5HRoZ8v+BgxxO2YoWoXeKlYNFCz3k3zbl+QI/O+RBtxnhr3Q8ib74p8uCDHcZt2+5fG3eI643XRZ55RvZuzZa/PP+GbB8+qpV/ERysibq1FVwTEdX9+0ve+FvmfZxUzse0adPYvHkzzzzzjNf6P/7xj3zxxRcsXbqUhx9+mM8//5xrr7220/6eeeYZQkNDW/66du16vFPywQcfTgFstmLqG/YSFTnhhPed2f0sDlkOsDj3RzaVtCZyG9f1bBKDu5MY3J3gwBQqlVYXucFoYuCZV9AlJYOtP3/OoT2bjzhGXYOD0oL6TiMQag8cahHOsjVWsWnx26z5/iUAQqJCqa+pY/eqnZQdPEzF5l00pKRz6O/Pk3/lHcjOnVonzX1HRbHkb097TYLmaOZveEsGJ3hNINfSd/vyzEx++mwG+g8/4k97VlAWE4FuSHPCQ0XbnmkWA5s6VdOdycxk/SezvfIRWuKU2iWjM+j1ON3tkpsdNZJDwS36jseamUnuu3d3zodozk3TjDvugNtv7zhum3YRxhD0d/4VHnoIfZCZKEVHetcuLePx7LNaxMyjnls8iu5/NHHK7wHHs6o5fPiwxMTEyLZt21rK2ns+2mPx4sUCyIEDB7zW+zwfPvjw24fVVizl5UtOyViLc370Wl5RVSz79remst+xY7FHfd6ujbL5x4+lpqK4o3FurthCwsU9erTnG/eKFSKKIhISIqqiSN39f5eG2nJZMftZcdjr5dCenyV/3yqxlJfL9sVbZO2c1S39Nb9113z+tah6fWt0w6efStnYM6SipMZTwrvpLdySOrDjW39urqiRUVKZnuE1oqQ8pZ9XG5kyRb5av1FERNSMDHn/p8Xi3L9fJCpKGtIGdWqzYFuh9r2dh2NRJ+f+UNlh2Xhgm9dIDq+ej6Z21tRUrx6W7DWveh2/Pd5a94NHf51FkDy5Jc/j+6J1m6W+utrTU/M7klM/EurXrBG37SRF6vxKnDR59U2bNlFWVsaQIa2pxN1uN8uXL+e///0vdru9VXK3CSNGaMSvAwcOkJzckSDm5+eHn99J3G/zwQcffhVs9hLq63YTFXXGKRlPjqATIkBe4R7K87fiqsqB1Na8qN37DaWbXzjuXimUxcZiUPXsuO4WSjJGElhaTP/kXjS+9DShCxfj/8WnlIaYCNiZTbeoSPZ+Nx3dG6/T74tP2P2HwQw+809UFOylvGA/aWOuI3vxVvqOGsDSz5aw4a1P6Z0cS1gToTJ44yaqe/RBP2c+oT//ACtWELl7O1Xjh+HOzyfnhQep2zwbU1EZCQN7Mf2eR0jffojQr78ju4nzEVhUTPqADFbdfAVjd2/H/9N3KDXVYSwpI75/L5bfeBOXmcxeFTkLC0uY37iabmMzSfj2Wx4Yewb3905h7/W3cVao/sgqnu34CHX1e9lw2ImIiqCiihtVdVNdX8v2EjOHnTqGDOxP1vP/JmnBYsK+/pjStCnsbygBURFVEAFDaRExPVMpf/h+elrrO8xBrajk55/eIXFELwx/u5m8K5tF6RSa6b8KChWFhazJEfzKS+nesw/7/vIQkauzML/5PiW7DjHgXw/QmNiNW+yNbH3gz1SP1Z43NTo3B9YXMrBP2pFF1X6HaNH7+L3zPo5nVVNbWyvZ2dkefxkZGXLttddKdna2V5uVK1cK4OEtOVErJx988OHkwmYrOWUej2YsyvlBluT9JA6npxZKZVWxrFvxiezZtVTE7Zatm+Z0NG560y1e8LYnt2PFCnEb9R0TqjVxKqz1Ftnx/j9FDQyUTdOfERGRLVkfiqq6xelwyKrpC0Vyc8UZHCrO4SPE1r1bC8/j0K03ibtXL9n68wZZPXOVWBO7i9tgEPe40SI9e4pccUXLHMRkkuLhgzsmdVuxQlSTSera1zXZWNP7eE0EJyKy6/NPO56DyZPFHRKqHW9goMg333g0afF8tMOr22dKsWWflNbmSGltnlTUF0hlfZFUNZZKWV2VVO7aJrZLLpKqxjqpsdaJc8gQWfPeM+JssIrLZhe30ymq2y1qTo7IlClSN+11bT4Gg8jo0S1J5Rpnv9rp9feKth4MkVb+SlNZ/ZuPe2qTiEj9zHek8an7RW1OpNf2WvzO8VvlfZw0zkdwcDBpaWkef2azmcjISNLS0jh48CBPPvkkmzZtIi8vj++++47rr7+e8ePHew3J9cEHH367sNvLqKvbeco8Hs2Y2OM8hsaPYu7ejz3K/UxaeGFKvwmg06G6nWzeML3zjtpxElyhQVqenXY8DCkvxzF8IP3veYWCcan03lSgGaiCoujYtWIL/cdnUH+oGGv/QTi+nUbFPffArl0A1I4cjlJRwcDBPRh12Wg4/1xUo5G6WV9qb93z5mmJBQGio1n3zRteuSBqZAR5X73XsS46mpLn/ubVBkBt6KhbAuDqP0g73kcegfvu836OmnU3JkyAYcNIXLmFuJDexAT3ICa4O5HmBCLM8YQHxBAdFE5EQAh+eiPhAUGE+gdhuOkmEpavwxDoj97PhM5gQNHp2qjcNp3/tDQtmd7s2R14F78IzfyVJiiN1g78E/Nlf8ZvyBmoRh22/zzY8Vr4cFpxQpNLmEwmFi1axKRJk+jbty/3338/U6ZM4fvvvz+Rw/jggw8nGXZ7ObV12URFnXn0xicBIaZgwgM9k46ZzWEMH3tDy/fBw/6ATm/qaJyVRcR9L2oJ1JoFvxITkeHDOJg921OsKzERLjifvHcep/Qfd6ALCWZd/whEBBFNWKyuykJYTDj5a/cTnBhF0ZZNRMTEtDzsIsdNoOymVoKm/7XXQHAAgniSRI+U1C0xEVvGUAJ0uo7z68ymaeGQ9OrrWujrAi07blvRMlVVPUXLvCEzs2VRNvbNbzrWt12gXH453Hxza/ljjxG6Zafn+M1zmDGDFgprcrI2718qN94ZCbVpSyXgny97irs1QRccii40ArXeQvVfzkQ1geOBO3CX5B3/HH5DUEwmVLv9dE/jV+FX53ZZtmxZy+euXbuS1ZRS2gcffPj9orZ2C9HRJzjb53GiqK6BuXsPUtnQSKOqMiI+hm4hwRTW1VPa0IhS4yYs34XNshh0CorBAGVlGAekc2hKJlEVARi//Ja6sET0RYWErVhFl+sKUfflUvLSP7Bum4+hsJTQigJyt61Cxoyk96fTSbzxChRFQQQOZR8kODyUHZ/OJ6pvFPLCMuK3byWgsBgMBpgwgdDiIiz33EPjjbei2J0o0TEYnC7M5/8B2bkXdcZ0mDQJJe8Quqwsxl6+D3ZrGWq1RY4gqorfqjUkXnEjsvsAzJzZGnmSlUXc7m1wqLgjZyEzk8OXXEi/CRM75EAx7NqKMm6cJjB2LFyHmho6ZdscIbHcnmvPZnjG+UfOwdKed9G4++jzaQNpFYpvTi2MOBwo48YhX32F/f6bCVj0M5wxHhRdUyJHBbrEozgcBD7xJoFbi5AZM3EUr8XPZkd/1FFPLQ5XVFFSXYPL5UJ1unC53KguFy6Xi5SuXejeNaGlrX9q6u+e9+FLLOeDDz54oKHhAEZjxOmeBn3cNnonxuNvNOBvMLCzrIKNxWV0CTYzOC6G2vxc+lw2RXswuVRwu5Bly1BWriKptB6dwYlSVYn5+1moST0RVYfzkb9jq7EQuWsv9ivT0BFGQ4OD5KGZmPwCCbzrb/RcW8BG/Vsk9h6B/nA5cRefjTUuFtVmoTY+ko//dRl3F8WhvP0271x7G6aKCi555Q1+Th6MKAqBJcXE9x7Euo/+w8jpM+h3ySVagjejAbei45MpN5Pqhqiv5rA6IgVFgfgtm7ikto5iWxVxof7UvfgweyP24VdUSY/0BL64bSIXrAsg8L1PKLBqAmem0lK6FZXjPKBgKf4ec2UNtdN+BEBXXoqpezK1Lz9FwKKlGL76jIaINqqY/nrm5OUSWFBM5tLF1A8bQOiuAyz7520YV//ocR0CiktJLSlg3dtPkKBzk2opY/1P7+FfUkHv0hwOb9lA/M4yoivKKfj0Y48FTIC+DvuMN4ioqcZZW4Yu2B/n0w8z/+qrCZrVGmbb1hliqPyRMx58G1dSPIrNjuXGC7CPSNUWFIpGREWnQ19cQWj1Iao2f47++pEYr38W3Xtv4+4dj2J10HjnebiSovHrGoT89UoMq3qhe+cl5g4/gymDRlCb1BO9zcq+G2+lYsTI47w7tQUQuhO3hIkwGegZE4nBHIDOaERvMqIzmjAYjazZtoOdOYdbxwbU+HguOGGjnwacXPrJ8cNHOPXBh9OLsrITlwjr12Dbtq+OWH9giReSexOB1LIoTyOcdukikpYmkpsrhYP6S01SgthDg0UNChIZNEhsM2fIujnTtVDcILNU9EkTS3IPcc2bKyIirgP7pWyYFp658vVHRcLDZefBjfLNp/8Ue3CgbF21UXbO/N4zhDM3Vw6ffb7kWho1omRIiJZ8bvVqkdBQKek/QGvXNsx0xQpxxsfLzB0rOopyTZkiM1f90NGmJZw1o2P46YoVohoNoo4f1ylRtaWPZiLnV19J2XV/9NrGHREpttRBXsOAG/r0PnICtaZrIiItxzZr2nzvbUVk9Q/vdRQU84b2JNQnntCudRs7256NYs0c1tomI0PcGzbIodFjj97/bxw/V/z2npEnLdTWBx98+N+G3V6GyRR9uqcBgFt1HL9Re45ERASkpADgZzQRGhSOc+AQNsaYiCqqpPvlV9C3X1/sNheupF4ceuEDhqTFwaWXskWv4Nizh6HxmliVvsEGIvTvOZT+ukhU9WW6XTyJ0EabRqRsA0Ew6nWtc8rLg9raphTvTa/57cJcRYQAo8FTlGvsWPj+e8bl7AP9YzB8uKdUd2YmZbe+RLfK1R3DWcPDkQU/YqishjFjNLurrtL4Gc0y5Xfe2Trpq6/G/OTjGiGzXR4SdeQoSu99nG7lez3Hyczk0CUX0k9vOnJIbzOaji15wXdw5blHuZh4cETsljIKV/0XU1B880mGv06ErLcBQd8bQiOMVGW9jSGniIjaMiq2zCUuqM2xTJ2Kbvp0woLM7Nyzj1SX4+Qna2uS9veQhu9se+o44K9TsLpVAvQnlLp56nDy10LHB5/nwwcfTh/Kyn4SVT39ktPFxXtkzx7vglfNODjfS/h+bq6oUVHiTMtoEfxqFqdyBvhr36dMEbvdLo4PPxQVpLp/ijQkdBN3QKAU7MkTyc6Whm5dxRUeLjJihIheL6LTiaooIn36aBLdTW/dz2XNEfnqK5GHHvKYxsdb86XMatfaRURooaY6nYifn8x483Ov83ZEREhNxkCvolyzVs33nup+yhQ59FOTwFY7r4jt7DPFVlfRUejsCKnuCx+4W+SZZzrMzXn+hZL30wbPcZr6agn17UwwzItA2LT5qzq2a8Lq+e97FRSrL90n5Vu+79TumFPZ5+aKOzJSagYMPOEp7zud17F4co4TdU6XrK6uOyF9nSj4PB8++ODDcUNtdNKQV4ir+Eda3s6Phua3xubQyvbfj2Tn0abtZy3SxHLAxXfzXyMhJhKTOYi2AlTGsjJ63Xsfzh69UWw2rFfdhGvICPS7txFcU4MSXoEaEgTP/gPFUoPU1yChZmqfe4zg3YdQQkIx2G0AmA/m4TQEolgb6XLTNbB1K/tuu4uQFcvZedu9DHv5KSJyDzLr2mu4SqfTQkWnayG+9WUlfBuWzlkzn2D5oPE4QkIB2F/v5ntbCdElxQxN6Ys7wJ/8M86k28KFmBf9yOvDx3mcjuCiAjJS09j84miGryzFPPMl8pIO4ldQSc/6HGyOVVDT1etbekFZPSVLcumSNpb4xK40pAzAUGfBWJxPzfgJhO89yLq3n6Y8bxWBBcWkNlSSU7yNcTV6MBo9okiqplxCl5ETOl6v1lPf6rG56ipoaKD37Xdge/5pDCUVWK+6iMr7pnqYGkoqCE/pRvkjf8K8bB3GD16lccJ5wGivt4bbUUTZoD7sfOnvxPyYRdA375ETZ6exqpCAHZX4FXmJcAL8SktISunL3kceJWrZUswffESuQ8VaVED30GCNKwL47d5FV4sFZ7fummfsoos0b9kJ9Ep0il8a7eMFQQY9DW71hPR1WnAKFkPHBZ/nwwcfTg8ad1T8JrweIiKqqsrBGSsk9523RY2MbBUHa04MlpsrtsGDtc/txMTU+DixLfxQJD9fVIMianiIWH98W2zTXhG3QS9qTIzULJgjVaHBooLkde8qKogK0pDUXbadc4FsPusCqZx0rmQvWS11Q4eJNShIsosPi6Smigwc2CL+VTYoRRv7L39p9Rg0vYGrmZliS0kRa1qaqJdd1iK25QwI8Exy1uQdcUWGS+OgNK28rXchKkoaBnYirx4VJRXpw7X2n3zS+oY9c6aoiiK2seM8xbWabTLSvb71Z69Z6vV6NJZbJOeH9R0rcnPFOSy143Vo16a9QNjKOdO9jtNZe6mokJn7S2RdcU2rh8PLPeFhl54uakSklGYM82y3YoU44uJkee5hjYej12vn+mTxP47VI/MLMLe0+oT1dSJwUhPL+eCDD/97UO1uFFNbcahTC6ullrwf1pE3fz2589eSO3ctUaP64HbYYPy4VnEwbwJV7d8mRSAgGGpqUI1GsDsxjrkGQ5dBuP0Dkfo69H6BmEKDtfb2agDcej2rXnqD5AO76V9wkNC1q0k9exzmTRswNjaQNnEy7N8PQ4ZoPI4LL2T5aw9p81q3ziNlumvMGLIefYKCRx9Fn5OD2+3WKtLSKB8ypGOSs+HDaRg9jJLP39LK2yZ2y8zk0EfPe0/ElpnJmhe/1ubw0kut5fX1qHo9snRZR3GtzExWTn/ba3/2Bu/XR6eDTr1hOoP369AZpk4l/ocfj96uTXvefx+zXo+h+f5so03SqWjZ5Zcj8XEs//izDu0MOh27qiwaD8dk0s7FCfRKdEBmJqxc2XkyvV+IYL0Oh/r79H74Fh8++OAD9pwa/HqGnbLxVJeb4nW7tMXGD2up3HCQrpOGkHTecHqcN5Kel4wipEsM7tq61nDGdg8H4/79GiGznZgYdjvGO/8NI0fifuh+MBnRn3su+nMv4NAFfwCHA+X22wk8XARAt5J6UEAnMGL3YXAr7MzIpCEinJzMM1j693uxdU2AwED45BOIjW2Zg+JlXqCJezlqdZgiItD37YthzhytIjmZ9c88A9dc42ljNpP34Uv41zdq5Xfc4bGYAeDqq2HRIu8Knc1zaNbTuO02HOYg/A26zrPkeunPz9zJBdPr0BcXeqihNouK6Xcd6Hgd2iMrq9WuZ0+2X3ZFJwPhXVDsoYfwN+iwutzej9ub3XXX4U5J0R5ybdslJqI4HPzxthtxXnyxtviYN+/I8z9RONI1/AUYEhLItjrrCenrVMPH+fDBh//nEJcKioKiPzVeD9Xl5uD05cSPTyN+RH+Pup3LFlFX1frDbK+tJmnhQurSBxB64ACbn/wP5VlrCCguZlhsBEqzfsNbL+Co3otSVom+ezTLn7uXhC2HCd20FX1oIPuvPoOEUamoKzZTnjmB3PvuI3nqrURWlJB372MkvfYkiuom5PF7tQdRgxn/shx6BgXx/YjuZL5ZhS3ARNHIWLo8O5vKsT2JW7qIMdlrkYIqGl5/Ftfe9YCCrqCIoJUrOLOwGP2eXdT/9z1cBwrQF5YQtGQpo3bsQs0/RMV77+MoKMJYUkbU0qV0O2cPIftyYdZs2qO2soHdGw4RPvFiDE+8gOXWv+JXUkr8kqUM3HIxlObCW2/Bd99pD+DXX8fw4N89hb0mTdIiLwDFXouzvhDduRPRdesGGUOhsRHz1ZejDhuPolM8vGCKoqA6nMi4cRrfpagI5bLLYPp03P16YVi5Er7+uvOIl3YiZeEvPN3p/eFyu3C5VRRFB7TOw6BArcOFVVXxz8rCPWYM+m3bqPt2Ok5LOaI6ERFUQEWBmjKis5Yx5orLUPPyKH3tVay7dqAvKCRy8CD2/Oc/+M/9nvTp32peiSPN/0SiXZTTr0Go0YDF1XgCJnXq4Vt8+ODD/3PYDtbg3yvslI3XkF9JQ1UNBZt2YNrhR+LIgZhCzZCXR5+LL8M4eEgr+e/Cy1Bf+S/BoSEYusQzfMMa+MMlcNkFuFQXhhc/09o9+iims/4Iu3ej7vwP4/79AQFON5bifFyp/Rk9+Qb0I0fhrLWAwUjhtOnoIiKhspTggf1QXE7EYEQJDIQPP8R64e2Y7Hb4+Wf+8uN8dFYbxMbg7xeC9YoLCfl6Prq6RsJjA1EjwjHOmItz9DB0BcUE/eFPICBWK9arrkW/aiW2jJGoVjvOocOovOgmnPU5+C1eTF3f/hjLKwhI6UvQjm3QJR5uuAE+/ljbmml6mzdt+YLILoGod92K3aVicgvowDpsJAsuuYIxG9bQ75ZbcJr8kPSBVF56OWGKDuPcubBwYetDtam/7ktfxq5WoowJx7QmBdsLV6CUVBF858ssHDZacxJ4OHOEgKo9hB0sgu/XouQdwFxdR8OC9bgcJqqXT4cEA4mPT6dodG/U5i0twFBcRlRFASXLp2PKOUxMXRW7DpUzXsTrNt/HS+fRx25j1LW3UZ+chN5qY//tf6Jq1HDUMD82lBSTMiyNHe88Rdx3PxP0w5c06n/GkHwlfoXFJFw6FWfvHugstbi6J5B7x5XoLf4EbNiAY/x4TOUVmJdl0WXqjcQDFBZqnoirr4aXX4YHHugQavyr4M2TcwJxejZKTwBOPgXl+OAjnPrgw6mD6lalcaeX8MiTCGe1TSrmH5TG8gax1tTKgUWrZffcJXLgva+kbMgQrVEz+W/FCrHGxEhRdXWr+NbmzSKTJ0v1xFGt7d58UyN8rlghjrgYKSjJEcnPF0tUeIvImEyZIouvmSI1cV2koXtPkcmTNeLnn/8sEhoqDcPPEnnoIVEDA+XHj1eKM2u5SEKCLN6+TOSNN0Ti4loPolk4a9nzHUXBJk8W99lninP9Rm1u7UJTq2Ys1/poWz55slgnjhRreZ4W/jpokMc527bty44nsjkM981XZc2DD4jDZBLrhDNl11ezpDoqRmq6dmslwbYLg92yaa5HHyIikp0tdX27dnrd1IM54ggL9SRO5uaKZfzE1kbN16H9PNuH2i7dKk6X2+s4321YfOTwVC+E1NrFT3WsW71aJDxccn943/McrFghjRERcufXM7U2AQGtc/Y2/984FlVYfjNEcR/h1AcffDgm2A/U4JccdkrHNIT5cXBbBRu/3UfljlqSJ46i70VnkHz2KAL9Q7RG7XgUIm2StE2bpnEknm+TqKyZI5GYiG3IINSmpG622Cjc27a29LPh2qtZctczWBzAjz/Cli1w7bUQFETBDS9Q3+tMJCiM1HXfsyWnApvTjemwReMr1NZ23KtX8BQFa4EOLJXa3NqSRwG1vk770K5cczXotOyv5eUew7hcrk7P55lXXYs0NCDduuF/ztn0S++NX0gQ9oSEVtd+h7HaoJknMnIkh+64sNNxFJ1C/cBByPIVqO+8C0uXIt27U/D4i62NvHFVoAPhUqdTtKR3x4KjEUGnTsU4e03H8tpaT7vmc5CYSOnAQTx94SQIDob+/Vvn3Nn8f8PoFejH/sbfYZK5k78WOj74PB8++HBqoKqq1G8sOS1jVx+skewvd8u6D7Jl/4I82f9TnuR+ukKcwWEd3qzt4eFiGzHCo0yioqS+k/BTV0S4WIcNFTGbZe+ML+WHwxtbbEpT+4lqNovt+/keNhIVJY3928mUN4XTutP6auV33tkhnFbSk7xKjrsHDRQ1MNBrWGWL56MtcnPFFRYsruFDvYZjbtnyxRHP54HXX/fwLux67gWZ8a+nOm2/1Zvn46uvJP+GszsfJDdXqiZOkDVLFsn65cukvm+KLPv4U1ny7Q9HnJs3T8V385ZLo83htXmL5+M4wlMtm15pHaut3YIFrZ6PJjTs3SvWsLCTEvp6OqCq6m9Gat0nMuaDDz4cFY5Dtfj3PT0J5MJ6hlK9pxh6VmCjEtDhby2nKLkne1+9n6RvF9Dz4osoT0wk3OHih6tuJbChnqiv5pA75gwus1ioiY7CLyQQx7P/pMa/BBEwbd9LZG0t1po6JDgYv5dfRR69gfU7csmoqUYND8YeHE79C6+yxS8RgODszfSrsaDGgcTEwPvvIxMngtuNAtgU0If6Yd21koJrbkBZ+xoB2w6SZKmmPk4hKMwP14sPYovagW5LDmZLNfZYM7qQMGzPvEihKQYAY0khPW67FkNsPI0POSm9+o/UDxms1ZWVEdsnmcNv3U7U9OXEX3whjWnJ6Kx2Sv9yOft6+rN/w3SPcxhYVMZZf3oYS8+uxBaXUxcVyu4HriNmXgJ9Hn2EiB6xNM74hJLbr6F2tDaOgoIghEYmdbwoV19N6JMPeZVXb0Z4WCQjz5ioffnrXYwrLmBOZtDxXfypU4n/7gPeDS3Hz+TfwavRUJ9NUM1q+g7pypZXryH+hz6ETH+JHdH7SLUkExIUSDPTQVdUTMQNf8a/Wzyq/W1cl1yMvn8/nI8/hm7xEnSffkLhyMspm7sMAFNpCf3uuwW90aCFR8e349j8DnG6wuN/NU7BYui44PN8+ODDqcGp5nq0R/X+HVJfekhU1S1ut10ce3dJwcQBUllTKqUbV4orNETee/sfIuee27rnn5EhMm+eSEKCfLV0qyffQkRkxQpxRUdL6bZtnnVNHI29077xaiMJCbJ56bNSsvAzcfv7SXHWNCn/+AVxxUTKilWPdmrzw663vI6Tnb1ArPt3etocK48hN1ckPNxD+Gr2mtkdT6AXfkNb+zULnzm6cFZzH83en67RGpflxyNL27fFnDVzPPvzJgDWbkxbWIg4x409Ypv23pKy7GzZtz2783b5+SLp6R3sDs/Y4tG+4cyzpXL8xNb7ygvH5veGn38jvA8f58MHH3w4IhyF9Ri7HOcb6wmGuB3ojCYURYdOZ8JoCkCn1xMRGkNMZAL6Hj3JWLUHnM7Wt+OpU+H771s76UTDQjEYOte38MrRAENkN2LjB6OLjiFudS5RvUaiN/qjRsV3atNZf263C0N945FtjsRj6N79+ISv2vMbuncnbu7qo9u3jcTIzGTzzH/B2rUdhbvy8rxqfICWRM8DxyAAVp2RTu38uZ5t2o5x0UVQUtJqMHUqAdO+QoydH4rXY506leD5X7f2P2QIAVs2ErRpPRQXa+29cGx+b+gV6MdB6++M93EKFkPHBZ/nwwcfTj5Ot9dDRKRk7ZIOZT/sekv70PQGXTSgf6ey4mVpg7xzPsLCxDFkiFcuRmNKSqf91Q/u49WmZkhyx/KmZHHOQD8RP78ONnWDNG6Jt3GOJfFZ+3afZ3lPRue1v6Zyy8CenR5rZ9LkK9e/4ZmIrq1dJ16bFs9H83mJitLq3367Yz9N7UrOHivllvIjJ71r57EpKyuWvbu8eD6Ock5bEu81RRWpUVFS2auvCIj093J//Q6hqqos+g3wPnycDx988KFTVBUUUJK9C32O9wRdHjjG/eS2ecfalrWHtKnLL9xPuSgYi0tIvv4W3D3iGVlnYc91u7AmxtG9d1fmTh7DsPpQQt7/hN1O7ecqoLSE1D79eOOOBzm/YC9h385ga3w3AEKKCknr15+8qTeRsH8/5g8+4pCrNenYqsnnEqfXEzrtG3ZExwEQWFTIgAGDKHi0G6nrnBi+fZ7GuN3oCisJHJTAT3/vT/flIYR+9ho7/YsILK5gcK8EXAE9WX5Td/7wUBa1X75KWVg+hqIKYtO7s+zWSQzeYCfg/fcpE0ExGDBVVBCTlkbp0w8R8vPPmD5/j6oge5uzIiBgKC4lMq03ZU8/gHnhckyfv4t67ngWrlrV2kwE/6JiUvr1J++e64hetY6A916hwJqHqbSCbr27se26sYwpDkH32We4goJBp0cpLsEwYCDWv92Jvr4Wv/vuxhn/MUpFNcalS0nbuwHJ/TsNn76Nszqn5Zop1YcJdNRTV7kPXe5egp02yssKQIS6Ogt7t+3CUFhEXL9U/HftwNFow/+Ov3DolVdo3LoZnaLTVGQVBWNRMfHrtuIYNw51fx6HX/wXtZsWYCgoIr6qiM1rZhNQWk5qo4U12QtQRUUVFVt9Az1LYsmr2K3NSwR9UQER/QdT9vgrmBf9gOnTb6nxS0Sn06HX6circeAodLFrkUpQWSE9Kxtp6D2IAxeez9mPP8ShQUOpnXIFIZ9+wV6/Vm2SY4N43uXN/1bcLtApnGwB8bb/lgCqAgIgctBJHfOE4qQvhY4TPs+HDz6cXOxavkRcTu+RBqcKOVv3Su62/dqX3FypHT5e+9xG30NMJs1TkZEh0ru3J9/CZJLSIUM863JzRY2IEFtEpDQOHuKZoK3JJn/EKK/9qSaT1GWke9Y12djTe3cYRzIzRUwmqUvuofE62tnU900RNSND1F69xJmbK47ycrHPmiWq0SjOlB6iDhksaq9kcRfmiXvPDlGjIkUdN1bUoUPE9f67ol56ibjsjeLau0dUg0Ea+/UX99Ch4pj3vdidDnHs3ydqeLi4wsLFkZourh/miZqRIe7yMnEvzxLVZBJH/4GiDh0qaq9kUQ/nilpXIeq2DaJeeJ6Uv/OpuBbMFHdqX3FsyxLb7E/ENW6EHPr3tXLo+buk7OZLZMeu6bJj1wzZsWuG7Pv5bXGGh0jDkL7iDvSXA4/cLXsX/SDO8HBpGD5M3AOHiOPVd8R1xtniOv8isX41U1SdTuyR0eIYMEAqP/lYCnMPSkHOQSmd9rW4DQapH5gmzq6J0nD+OVKcv0fK1y4Vd0SE1PTpJW5zoFTP+kaqs7eIOzJSnGPHiGvIYLHPmSt2q10cdoc4HU5x7tsv6mWXtfAd1PR0USMixD1+vLiHDJGfnnxVfli3S+xOtzTs2S/WIC2ZoOh0IkFBrdetnQ7K8UJV1dY/t3f9kpONumXLTjvvw8f58MEHHzqFMSAQveFIm+dHwBH2/o8HxQcOkTSgV8eKtvv20dEUPfx3jRdgt3tyJ6KjWfzOex3qlOHDKR81muJp33RM0BYdzYxps7z2J5ERFHzxVse66GiqXrivY/nWrQAEFBbBe+91sDn8j0dQZs9GcTgwTJuGMSoKU3Q0SkwMtsfvRZn7HYrDie6zr9H5mVEyJ6AsX4EyZy76N95E0enRmwLQm/xQ0tKwjRqFbs4cjP9+HJPBiNFgRBkxAlvaYNxffIn+sX+jTJ2K7oMP0Sk6lOho6p94A2XOHBSHE+XLaShBkSghUbBmPWGvPI9+yvXoXn4NY0g3/G68F/3ugyR8tpxu6ecSvbWA1JgzSO03hdR+U+jdazKGM88mcNNudB98RPe8Uvok98dw5pkErluPbt5cjB++iX7bJvRrV+F/83Uoo0cjEVEYv/iCiP++SZekniT06ElMQiISFob9i68wrF5D4NIVxAVEERWbhO6MMzj83Avo3v+AsPVbCAsKQzdhAoYVK9HP/Q7TU09i8jdhNBkxGA0YjAYUpVWCXbn8cpQuXdBlZaGbO5dJc7/A6nCzYMMeAv0MVPTujyMiEl55BWJiWq/bkXRQjgHNc1AUBUV3eh6rxq5dcRYUnJaxfwl8iw8ffPh/hKqiQsLjuvy6To4lo+hR4HZ6bsoE7N7mmZwsMVH7DNqCJDq6VfzpSHVmM4vf+YiQ+roO4mNH6s8+dBB+/kGedS020tFm1CjU888j7y+3a9LlHWyaxgkJ0bLNTpgAf/mLRvC013Xsrxk1NWA0espxJydTd9VVHQmVZjMFdz+On7iO/VgBxoyh/tn/emZYTU/XSKs6HUyZAiNGdP4wvvpqdJs2Q3W157xFYPhw7VifeQbKy6k/+wL48MOORFBFwWWzeZCC12yeS2lpDnstu1sTsHkboy28JJMjJaWlvVtVcbhVNu2poLq8Ab2fP+U9U+CuuyAgAMLDtXNzssTFTtBi/Vhg6tEDR27uSev/hOMUeGKOC75tFx98OHnYv37Nr+ugnRy3V0LhMWDxZ9/KzrVrZPfGtXJg3iyp7pMqDT9+LfZHHxDnDVeL/ae5okaES0PPHqKaA6Vh7kyx2a3idrvbkFEHeiVaFmUM75TsWZAxrFNhMvuwoV77s/b1IiQ2ebK4IyKkPi1Vc+FPn+5hU5/cRFL99FPPcFiDQVwpPb2O45U02TyH1NSONuHhour02vh9+niSR6OixNk33Wt/7gsulJofV2vfMzJa5OplyhRpeOEhLVy1fXhuu9BXx91/FXnwQZGoKFFHjxbVbBb3J594hgsbDGLt450w7A4PF3u65/yWffRvUadMkW/Wf6O1e/PNljGOWRCsWeRt8GBRQYrjekplUn85fM9b8ua7H8vh9NFiCwrR+jOZRG688cj9/VochUR7olG3bNlJ7f9o8BFOffDBhw6oq6wgODLq13fULMfdnC31ONFYV4U56hC9h0xGVFADAyARTENHIf3SUC64AnVwKtTVYY/yRwkNpu61l9gaYsflchJQUk6/fkk8dufjXLN+HaMvupjS1IH4WSw4EpL49z9f4sachUTO+ZzdfbQc8eEbtjHGYsHhtlIXHk7ly6+wMr4bQYWFnHvlFFRUlOoy6i7KhM9ep86/BOO2vUTV1OAINaMLDKThlpvJ69YNvy5d6J6XS4DFgj00EpM5CMcDD7LDGEXQzmz61dRgDQrBaDZjf+VVMJkomjaN4OXLiXe5qBXBHGLG/uy/KfG3ELBtN11qaqiuKSUw1J/6Fx5ib/heFBSCtxwgzVKNI6wL+uAIHE+/ToWSgr6kgIjkATiVRlyT0zBlH8Tvnj9T+e+bibn1WdDpkEYLqCrcfDPExmoJ+IKCUBb+hGFbNjYD2Hr0RP7xCCErVoAC/t9/B2/8V9tKakZeHgwbhjMkAF1ydxxXnIctpT+KK5jAhCTYup2GoBDMf70Hd7deFC48hH/2BhJUFXdlJdbgYOqffop94gYgOHsnaXV1uBVFm9eHH8KkSeh1BpSsLM45fJCq+ofZcduV1I7uyqCNyWy762rOvnojjquvRCKjOXzDddRkZHS4t/xKSknqm8LBq/5E/5eeJyBjFLbRfUl451VuvOgteOBW6l55A7/mLLbbtx///f9Lcaxh0/9fcAoWQ8cFn+fDBx9ODvZvWPvrO2knxy0PPXTcXezb9J1YqtqEP4aHizvc3Br6+eabWrK3hARZue6NjgJfTXN4c9VST0GuJqGt7zcsFqfL5UkibBL/2r79K6+J4CrPPFNqd+3yTATXbDNnjkh+vqiBgVJ8//1SPWuWyIoV4o6NleKszV5FxlYt2qSV+/uLREZqb9oBASKRkbJw4UKvNrPXzO5UzGzjquVe590wcZjWLjVVZODAlnKZMkUqP/yvdm4yMkS2b9f+n5srzvMvkOI1u1rfxJttoqLEnhArYjR29L5MmSJ1015vFfKKihIZMUIkJEQa+6ZL+aEyUduKdTXNe/XC7V6PyR0XJ7lLlnrUrfjk6Y5egjYiaA3nnyN1qX1aj6V5bm1Dh5u9L03EXwkOFkdijEYsbWrvGNqv9dh+JdH0qDhOmfhfC+vu3eIoLj6pYxwJPsKpDz744AFrXS3+QSdYVKx5X759srWjoKG2jJDw7q0FZ55J9bKnWjkkd9yhJXuD1sRt3sTCmtEsyNUktCUIBr2+ExKh4rU/l8tFgN3uNREcaHNQIiOJ0+sJu/VWuPVWlNJSIv90hSbLbTZ7FzMLD9ckvFeuhAcfBLsdPz+/4xZA66zOb/NezQu1fz8MGeJ5pA2NrcfThnfhdrtRVXfLm7itsVEL28zMpOj9p6BrV5g/3/u5bn57z8zUEvwNGYKpII8gsx+KV7Eu6fSYxOU88nnwwhMpu3hSRw5JWw7Ss89qInR//asmTmc2YyiuaGmqNtSiGPxabX8l0fSY0C6p3smEX58+2PfvP6ljnDCcgsXQccHn+fDBhxOP/evXnJgwvPay178gBfnCLx/z6E+97DKp2PxMR8GpqCipGpDU6Rvja8uXen2znLPOS6KzowiJVaeldcoTqe/bJoFds5cgNFRUEFVRtDfsPn08bCypAztyPpp4EJZ+/bzOoWJA3045H7UDvPM3Gs8arX1u9kK1OR+qv59ISEiHRH2u8AgpT0oWp7+fbHvycVnxzltSNmigyJQpkjv/fe2aJiR4epqiolq5Km2PacECUfV6cQ0f4V3oLNW7EJwaGSnWdgJfyz95SvPUGAxeeSyuyAixDPLCfWnLQUpLa/V8xMWJZGSI5aZLtM8iYt+4SOzbT4AH8FjhRSb+pHpaRKQuK+uk9n8k+DgfPvjgQwucNhtGP/8Tk4CqfXTBHXccl/nWn1cTGT+U3ZvnAWAsKiVpySIcu9fgznuM3c/dRM2GN/ArqqTHgARm33kOE7f7E/DFZxQGBQBacjSA+nxhRf5h+vTLYPu/3yRuyXcEfTKb/HMvo4zlTbF82huyrqiEoAGpzLprIr2zKwj98i2yw2oILCpjYHoyX/75PC7aXEfwhx9wqImb4N/EH8g+7yqGlZWjf+czHGdfREBJNUpCD/SShy3IjNFph4oK9q5dhrG4hLj0/vx4/iMMKv+ZqB8+x2ip5XD2NIyFZXRPTiC/d3dCzj0Xv3ffpbSxEWNZGfG9e/Pt1PMZX1KL+csPyAnV0s37F5XSKz2Fr+99igm7dhH8zffsi0sDIKCwlD6Ndpzz/kXQX15Bsbtwf/s59sQo8t79J47nZpCybAkbnvqcmCXfYf50DiUX/ZGk/hlsvmEqPQ5uITx7B7U3TqXuP/8h+sOPqK10UDRgAIFXXonukUeov+ZadMVFhPbvz6HxwzDUGwj+8lsi1qzA2jcVc+5B6hJ6UPLAP4jashbjx59g0RkwZWcTW1ODMywKW3Aw9U89yX7VCYqCf0kpib178/35NzC2tpjgDz+nQhdEjqWclPTeYHBTfUYaIdtyCLrvz2yP/Bt+hZUkpMaz/oFzGLq1Dv8vX2VH2S5C6/Wk/bwQa7++mPPyyL3lRsI3bKIkaxH9ysuRmmrM2dtwRkWw++V/4Gispf8FV3MM8nonB82elpMRWfN7wylYDB0XfJ4PH3w4sTiwcZ24Xa7TOgdVVWXLe1+LKyzcU9o7N1dcl14q3639SXt7v/32Vj5BeLhseeVJrYPO3hib9/ab++zdW2q/3y5um9Nru6rB6R0FwyZPFltYsLjGjdX4B99842HTkDaw1WbzZu3tPChI83z4mTSZ7i5dPETGLOlpUte7t0hSksb5yMwUSUkRMRjENrC3VzEzR0pap4JqlRlexNFyc6XmrDFi37NT1EsvEtfzT4jz2iniMvtLY2ONTP9srqhhYWItLhWrwyXuoUPFuna91J9/ofy0fHOLJ0ZGj265HmtmLBJ7rUUctRZx1NWKs75OnAt/EtVkkpL+GeIeMkTc3bqL+7zzxWGzi+sf/xRVp4hj1AhRh2qiampxsahLl4rapYsc+O5dcebmiBoYKM6SYnHY7WJftEhUk0lK0weJc/AQcSYnS8Gug5I/e764DUaxhYRo8+nWTeOxNJ0H1WQSx7BW0blN05+Vwx893Xr9e/QQOf987f5JTxdRFJGffhL7u+9KTd8Uqbn1Fslf9PMJu6d/q2jcvl2cJ9m70hl8nA8ffPABALfLiaJT0On1p3UejTW1lB/ORz/xTC8aIYKi6DQOyapVmkbFtGkwahQ93/tMa3Kkvfno6NY+7XaMc79EMXk53uholr3xD68iY9VDUmmY/x088gjcd5+HTcGzr7XaTJumaWC0SGmrmvenvt5DZKxy7m0ELVkCDofG+Vi2TNMvURTKX7vHq5hZ3WPPdyqotvKDr6md9iWq3Ybj3XdpdKs0du1G3sv/xqm6UHRGcsNyKAuvQtEbCQgIxWQ0oCQl4f/JR/gb9ehuvBH/WTMwGXQ43Zp3h7Q0LbFa0/VQDDpMwSEYg0MwBgVjMAdhCAhEiY5mw8vfoJs7F53Lie7wIYx+JvSTz0F0eho+/hBljiaqpnzyCYrBgOJ2k3Tt/RguvwLF6cTw0MMYTSZMfn5IVBRrX3kXw3dzMTidJMz9lsSoYHSREZqHTlEgP9+TxxIZTu2nreduyAFB0ZmwB/lz6K0nKf/yW1wLf6a6Zx8c06fDsGE0TPuaPYpKSFAwof95hsSJZx31Xv29w79fP2y795zuaRwVvsWHDz78D+Pwju10Sx14uqfBwa37GTRpfGtBG0KhqgoGXdNi4Q9/0EI7k5LguedaE1h0JgLlRVDLdumfO24xNbULM4Z5Cm8lJcHbb6NDwe60wcUXQ1xch74bDxZTTxDV512j1Q0eDPFdcMfFgMUCLhfcckvH+URGtopemc3aX5u5eoiCKSDV1V4F1QJsC9mZu4n6UDPLrp3AysLVrChYTU39NgzOasjKoten++ny0XqqHv0zAA1xCbBlS2tfd9wBzz2Hc9o3OF3atg7JyVr9kcJAm+YQWNrItnnZEBHRekyJiTiSkzB98WXHY3I4tD5374aPP4YdO1ps1GHDvZ+HwYOxDEyHjz7Srk1sbOscRgxHh8HDJjF5JCb/IHbuXMGKL2ci5iD8Dh9mw0efonaJR1mylAGXXY5yKoilvxEoBgOo7tM9jaPCx/nwwYf/UYiq4na5MJhO2w43IsKBn1biXLcSv2F9O2qEJCVR9+nHbJj3NPrKffh30zEiP4/GtF4EHyxgxb3Xkrp2LzqDHr1Rj96gR280oGv6bCi3YF62DBk1GiV7O+q308EIqqvpx7dZdlsVyMpi8P7dyMFDOL+dhmq3IyKI3UbY5p24zjoP2bMP62efITXV6Opq8V+2jPgduzDl5LH16meZv3AfU4tr6RKkQ1dRjjs6HP1XX9L4xymorz2H8w+XErZsGTFTNiL7/o762ivoHnoExoyGLVvBaCT6zpcg90FPjZSsLEJ27IGCvI7aKVlZZB7cjWn/IZg1i0ndRrdUNUodVNRqERUzZsDXX2Oa+wmq29XpNdHpdIioLX23vR6KtZPrmJXF8A1/JKD0AHw3ByZNaqkzlJbDwkXwyuuecx8xAlttGeY7H4DFiz0WN/qVKxlz8BbIPehps24dYW63thibNQv+8Q944AGtbuUagq7ZCftKW20SE3E11jPs4feIKChBXnsd/x9/JHJwKj/WlTF5wj0+jsVvFL7Fhw8+/I9i//rVdB8w5OgNTyIcdQ3Yaq30MR8i8IonUY3+qLX1yE23oF+0GN2kSQTmFPDIHR+iHzxQk9NOG4hu6RJcr7zM5EcfgxV7oaGBxr/9Hdu4M1CdLiTvIGFTLsSV2BXV4aT0yhvgnBoC3vsY8+IFuLsmodhtWK+4HuegoejKSgnsmcKKS++lmy2b4K+/4WBYBAoQtn07g+sbsdoUDKHhON54l0NhSRiLCkno0QfTnq3YY6MZ+MNjlN86iHV/7sMlry8ChNrQMMKsTtiQTfajT2DIzyO5Xx8Ct+3EHRuJ8re/4UxKpOrdf2P+76cEvT8Nuy0WxWzG9thjFFdVYSgvJzYhAf8DB1Ej4lCuvhbr317EPXwCuuyNmGssqImxHoJcAOTlETDsOtxxgaiHKrD+98+4R/fDvHEbangoo5IiKHMFsP7P15I/KgNECC4uY8rUexjRNZaaBiuu+HCW/+dmes74mbSLLiQ+OQrrI4GU334tDaOHYiwqpcc19yA6HZYAG7uvvgHTB5/T+6qrqUrug5+lhrqEBAqeuYAeK/sS+M7jhF55GY6EKPzySjG4hcqn/kPErh24AgKxpA/CWF+HIzqWvz35IVftXE2X9z6j9xVXURPdlQiHk8LuXXBcPYqQ954gdud2nANScAf64eoSzOJXL2TQpnACZn5GfpI/hsP5hCbGYP7mO/TbtmqiYXNmU/rUY5zz4mvoA82n8e4/fVD8/VEbG9EFBp7uqXSOk85AOU74CKc++PDroaqq7Fu/+nRPQ9Z8971smLtMDs3+RupSu0vloCFSPP0nsfXuJ9bkvrLv/e/kwGtfi/O8CzSDJqEwEdEEscxmjWDZXpq6jfiUnHdea92AAVIzeLD2ua1NU/s905rCENsJkDlj4qToYI13Ia9RKbL55+ukISlEnH0TxVa1u0VEa+vSGV5tSsZN1vru21ckNLRlHDEaJXfOSq829ZmTxFZvFfEi1lW0dV6nYmuSmyty7rmtx/rEEyJpaTJ/19udnzcv51rCw2Xh6uc7nrcm0bLvVs7SytPTRS68sKUPe2iI5NWVa9+b6qb/9II0jh0p1qAgrfwPf9CIoE02aliYvL2zoMXGcc55kjN3vbgmTZaStD4efbWd6wtLv5Cy6hJxDh4kVXn7pSZ7i5RljGg9vrbXtb0AWXPY7snCqR7vCHDX10vD+vWnfFwf4dQHH/6fo2jfHhL69Dutc8jZtRNTiJmMizLpNmg4QV374R8eiqKrxy8kEP/7/krvip0kXzQSQ0CT8FOTUFgznDExmtv8SJwEp7O17g9/wK9Z9Ky9TUMDybdcpCX5KitrdecDoOCyu72KXukM4Qw+6zMCr/krhjrBL7yv5/jtbGx2O4crqyjZvBcmTtQIpM0IDCR43nTv4lrNNBWvYl0dx2lBUhI8/3zrsbZLrtbpeWt3runenW7TV3Vqo6ut18ovvxyaE5jV1iIiGJrnfvnl1O7ZRnjKYL67vD9C0/bOCy+A260J0tXWIoCxmZdz+eUYCw/TY0A0etWF0jx2u3EQwRwQRnRYLIabbyH86xk4qqyY2r7dt+d2nIAkiMeFUz1eJ9CZzajWTvbQfiPwbbv44MP/IEqzllJ7BJdz86PlBCh/eIFCZU0N2U7oMnQc5euyCS0vY+i69dCjB1F//COWl15CmXgWfsFBGCoq0GVloXjJF2OoqIB58+Cpp7xyIbj2Wk+b667D8OKLHXPPNBFLrVf+ieBly6CgAC69VKtLTERxOoj+47mwf6dm07y18fbbmAYN7NhfYiLY7fT66z8hN9/Dxv+jj0hNH4T+T1dBzn6YO7fVxmgkcO1y+PqDDuP4Dx6CctaZkL29wziRf3oQmjgfbfkWnebZycpixLVbYe8D3s+bN5uCAqKz6uC9kZ7lGzdCSgrn/OHPMHsO9OkDr7/e0kfWm89TcmghbtWO31A9F79cwcRrHoMtWyjqnURAcz9OJwwdChUVVH85DX3zzXfddVp/Tdcy68V7ubxteVtOitLEZWnSl9n/2WxSZ85snWtnujOnOq/KbyGPy4nQ9TmJ8C0+fPDhfwxVX35J2nkXY0pMOPWDNyUhqw6PZkyoGVfvZOozz8CVVYC+vp5im4suMTEY5s2jZMQInCUl6PJyie3Th3cf+hdnr19DyKdfsNcviIDiYnoOGMT7z/ydS5YuIeSL99gdor3NBRSXkpbem11/+iN9to3A/933KG20YiwrJapnT+zjxhPcJwXDSy/TcDAHAF1lJcaGWpb/9wPCVT09qmvZOXcZfqUlJPbux7TbLmX44VJCP/+AnUEaYdVcXEp6ahL7H72OlFVD8fv0bSr9LBiKy4jqn8x3N95D390lhHw4iz2SrM2tpIDkfsPggkuIKj+M/s0PsFa50ZVrvJPsi68g2FaB+bPPyPP3R0TwKy4mKaUfix94gwFblhP0yWwOGHrhX1JAn34Z7LhrMqP2bUT/9XPYo3YBglJUjX+fKHTZW1CjQtBdfTlVf78WV7d4Qvt3Zfk1aaTtNRH47ssUNOaCgKmsgm69u3L4/n8Qsn0dAZ9/RlFQIKbiYhLTUtn05xsYfjAXw1ef0hDhh37TdiIs1dhcpbgiAml87SkOPnQTvTJS2PLs/UTNLST4h1XkRZ1PdEAicQfr8a93UGBtJDgijJLwKKZvzyd+8yYus9spCQ4jzO6g/vkXKHzwYeaWHyRuyRKGV1TgbLSidzoY/uYM5gwaSmBxGQN7dSMsOxsJC8Nv8mSu7JpEvX8QhX+6D8uITMyFDTD9Z9SHbsWd2EPj+Vx9E67BI9CVFRP88yLUfgPQ5x2g/m9P4pq1ouM927RGcDuFgnzBPwGMlcV0/8dtuGJDMSE4rz4XdciRPYlKWRV+Py9A7dcTXV4hjr9PRZ3zKs1L/G3lDZij+x/TP6O2y4aOS5ijLyoUwHH4EANHj0Z/msPsO8VJ3wQ6Tvg4Hz748MuhqqrUfD/v9E0gN1eqRo6V/L25ntyBJu7Cl0u2eE+eZjJJScYwrwJgjeFh2j66FwEwS6p3wS57795eRbkkKkrqeyV7SnQ32ZQNSulU5KtsYH+v41SnDenUprFvulebhn79O7UpGTjUq02dt/FBRK/XJN6ffLKVK3Kkc9AsaNZ/kNdxnANHepTbfpwjrphIzbb5um3eLDJliuzc9L1W7iWB39w1h70mz5uzfHnH6//ttyL+/rJywXyR9es1afU2198yfqL2OTRUO74j8Vi88HxE5KhJEPOWF8ieeTnitDs9bA8t/LnjeJ3hKOPtW/bt0fs4gairq5PCwsJTOqaP8+GDD/9PYd+7l8CMob+8g7w8TUNhwgQYNgwWLDgu8z0rN2FQ3SSOGaZta+za1bEPb9yF6GgWffSZV5GtwuGDtX10LwJgu5+6z6tgl+VeL+UAmZkcfuxfHZN8RUez4rWHOhX5WvX2017H2fXq+53aFP/jWa82BY891qnN4lc+8GqT8+rjHctjY+Hss1FTk+GZZzy5Ikc6B9HR1D/xutdxGl/+xrNcZ6TlTbtZt+Tdd+H77+l+68PaPTJ8eAceSp3L3XmSvM4SzYG2HabXe9fkiI/Xju9IWxqd1XWSBNG64yC5z30KtkpSzu+BweRlM+CXbKF4G+8U74IEBQXR0NBwagc9HpyCxdBxwef58MGHX466Zct+XQedvUUeBTVVFln2/jeSM3u+SESEJiluNot88UVravSoKClN855oTKZMkc+37/ZMLtdUt/+cM7TP7RPPTZkia2e/7bW87K23vfYlU6bIrs8/1b43v7E3lU/P+qhTmzlr5ngdZ9WiTZ3aHPz8B6822z/91KuNetll8tXSrV5tti2d4Vne5EmQ3FxRA/w0b0Hb5HdHOQdVM5aLLFggoteLGI2abVCQ1D/jOTf7zm3iDg/tkJxOpkyRrSvnePdCREVJ5YAMr4nmqlJSNI/NgAGtESErVogoijgDArS6//zHoz9naJg2HxBJTe00+V6HtPXekiA++KBIVJSoY8eJIylZGp55Vcq3HpCyzfvFA019WtuPd6SIlvZS/3fcoSVdbLJpGNTfexTMSYyS2bdv3wnr61jgSyzngw8+/Hocwxuf2G0sffVFGu12uo4dgHvnJlSLBcWtAoJcfz2qXs+6595hpMWCJTye8OBgbE8/TaXRhN5kxFRRQfSSJUzYezlqTg7lH76FrSQHBdCXFxC/bjMNwzMI2LWb6i8+RrFWYXTUEZS1jP6b1iFlVaiffQa1tSg2G0pWFmFbt8LfvJAtoTn+ojUy4qqrICuLCfu2ITnFuGd8i16kVSU1K4ux+3fBwcMdSJ1pew5A3gGvpM4u2buh8FAHm147dmhv+O1tli/njD1/gkP7O9gk790BuU02qgpuF1JUBGmpYLVj+9djuLt1Q79gAY64OIKWLSNo0ybkgftxfvYZ1NWiMxhQXE50WVkE79wHhw5oSq3du8P998O4cfg/fhvuJ+Dg009Su3oFHMojNKU3e159gi4/LSHkq084dNkF9K8uY3NePiFrqoivquXQN1mIy42xqoyYnv35/pbHGVSpJcLLd4cy6qrJoNPhbLBRl5BISZ9Uci+4mGF33cOOm28nIzyCr598kskL5hP02WdkDxkEgF9JKfHJvXHf/yQxTzyAdcAIHJdcj+mTb6kydKHrqN5akrg2ImssXaqRcr0lQczLw7lyHbW33Ef4mHSMV11B7RmT8AvxooeRmUnZn2+jW0V5a59txnLmHIRLLqUqpTeKohCTmKh5n2bMaCU0v/mm5k3MzKTwrivp3XOUVj55coexPOza1/8KqKqKTvfb2+T4VYuPZ599locffpi7776bV1991aNORDjvvPNYsGABs2fP5pJLLvk1Q/nggw9HgdrYiOLn/+s76iwaoh3E5aL2vceZcPsD6EIitULTSjCbkdpaFEBRVZTgYEYtmQPR0RTd/QDde8USNHkyQZPOBj8/1KAglNpaIiODcUVFEPjlN9SN0yTK/QvyCaivp0YECQ+j/u13yerWhchNW5hssWAINyEhZuS153D6VUFRGcbEGPT7DkL3nnDDDZq0d/OPeVYWgVv3UPXEM2RfdwslGeOJnPEjZ9RYcMSZsUcEUPf6v9jT8zCCELzxAAMt1eii/LAHBWF9/AnyG60E7tpJj5oaCInCGRyB4+nXqVS08FZT9gZiayy4zKE4AwOx/fOflBUX4rd3Hwk11dQFBuEKCaPqmRdZZYxBURSisjdzZo2FqpgAAkKDKX/+SX4OdRK3dScXWWqoig4kICQIeeZxyn/+mUDqCAoJQnG7cRsMGJ5/jgPPvkD3BT9xODGJ/rW1VEVGEh0cjP2VlynBjbjd+O3cRdeaGizB8QSbw1AO5mH3j8aydD9xRhOKw4EupQ99nn8RPv6Yupg4Ajdto/cDT0BDA1gs9L77b7B9N5c89S7h+bkwaxYpkzK187tyJTywlj++fBdGsx9YLPS880YYPRLMZnIzr2TEY38meO8Oev88H9XtZtxHb4MC5h4D6fLd7WA2M6ZnL+jdm7IVcxGTSlwXP/Q5+/AvyINevaChgaAXnuTwmv3oi/Lp6nKjB2274+WXtTDqyEiP+9W6K4fGuQsICzETef1Fmty7COTlEXrHtZCerh3jk09C3zbh1G37bANHcRE6hwODyUTBrh1EJ6e07qx0tnA/2oL+eLZ4msjdpKa2zrvdoiU2NpbS0lLi4+OPrc9TiV/qXlm/fr0kJSXJgAED5O677+5Q//LLL8u5554rgMyePfuY+/Vtu/jgwy9D/Zq14m5s/HWdHAdJz11VKvXfviqq09ZauGKFSEiIRoI0GLTtANC2CBISZPXX8zR3v7+/SFqa5rZPThYxGqXu82e9klEdURHisDV6JTBu60TkyzVqoGbfVrCr6dgWzFzqlQy7ZOsc72TYhATJX/aW1/Erv/ZCoGyqy5u/zqvN3HmLvdqoXbrIW2vnebVZvGa+WD77VtxGk1S+8am4Fy0WiY0VmTxZyseMFQkI0M7nm2+K/PnPIgkJMtsbubOpv/XfrdGEu6D1Oul0UpGW7nHeGhctkvroJnGw/Hwt0+yDD4pMmSI/zljS8R5pPi9fzfds3yRUtuyJ97XxunUTVVHkkf9+KMve+0TEaJRp81dpfVx5pcg554iIyM7pD0vNmRmy+/0HxB0XK9V3XyeWq88TZ2yk2P/xoIiIOPfsk/rMSbJ/0XbJXb5LXK+9rm13NMHVYJWKj2aLZeEar1s0BZ99J87zL2w9xuZtwmbCqYh2XttsociYMaKazVL2/rtStG+PlB3KD/Ec6gAA/g5JREFUFTUnp/Ptn6goaUxP6bhl1Ka+g93RcIxbpHv37j22/k4ATjrhtL6+nmuuuYb333+f8PDwDvVbt27lpZde4qOPPvp1KyMffPDhmCF2G7qAgKM3PFZ0QtJrhi48hqrtn2FZ9RjVy+6j7sAM6hqWI06b9vbmcrW+xen1YLeT+u8HNbdyWpomhFVTo4lPAeiNnZMUEe91Ip2Lb0Engl3i/Q1TUY4wPl7rFBOe4+flwfnnQ3k5cXf/SdMo8TY3b30pbYidHQTIFOprt6OGmZFVn2FZsgrVZsdRVcf2x1+B8ePh8GFta+Haa1sO84jkztpa8PPT3vJfeQWiovCz1HicN123rpSmpbbOKzoabr+95XR1uEe8JPrj9ts1rZCSEsY89VetPisLhg3j8cP7GD9yCAwYgNvRJMY2bVoLSTlkyDmsurM3NaYwbF2DCX35E0IeeQ59YhJlZ2ihzYaU3piX/USvienEDU7i0IAzOTD0PMr2FWL5YTk1MxYSfvW5hJzdNK/MTM1D00Q6Vp0ulOZtieb7omnLpnh/JYd+XsSh3n04NCSDguUraejbjwX338Tev/8B24rpHKxeyN6S79m47VMq0hPYeXYatqlTcLz5AvXTXqfh+49xJURjOHiYhqgo7FdfTfYLL7B8+XKWL1/Olu+/R7VYqGlooDEkhJqXXup47x0NvwVdkePFL1ndXH/99XLPPfeIiEhmZqaH56OhoUH69esnc+bMERE5qufDZrOJxWJp+cvPz/d5Pnzw4TihulxSu2jRr+/IG0mvzVtkezgba+XQF49L5ezLpHH7x+Les03UgQNE1X4Ktb/ISO3te8oUzfPx2mua56P5jdvPTyQkRFwpPb2SCZ2hIeIeNcrr22R9mpe3ydxcUUPMnb6BVvVN81pePahfp2RGW/8krzbOvukdyydP1siKvfpqx9fOprRv56TJogH9vc9tQH9xB/hLxdtPyv6s18VaXdp6PIOGebWpSE/3fm6iosTSN10773q99tadkSGSlCRunU6cGRniNpulYtYMKdi2SRrDQr0SThd885O4a6vE/eLz4n7sUe3zts2iRkaKvXdfUc1mcXz/vbj27Bf10svEabXJjtseFFfvXlK1fYu4IyPFbTaLOzBA3D/Ol3e//lEKSyqksLRS6hvbeNNEZN/HL4mz3TUtzPpUXHar1/uydPt22fTca1K/L8+zov39nZEhh178SNT2noemc2hJG+FJAm26vrbQ0GMP/26yqR0+XGvX1hvXbJeQICIi1v37xeXv3+qpOhKO0WOyb98+UVX16P2dAJxUwum0adPYvHkzGzZs8Fp/7733Mnr0aC6++OJj6u+ZZ57h8ccfP95p+OCDD21g3baNgCEnIImcN5LeEWAICCbmnBtZP/9pYiO6oS/YQrxiJcBgQFBABKWyEntMHPacYobefA222Dj87HZsPXtTe84lhPz8Pf4H9mCLDsE/zB/3C3/HFrULAN2WHAIb6qlosBIYEkb1My+w3hhB1I4tjK2xUBcbjiEsiMYXn+BwlCZAZiwqJbJfIpv/+UcSl/Ul6MNXOCh5+JdU0LtvN7698a9MOFjTIuQVmr2BjBoL9bHh+EUEUPfqw+xJOgjQxPmooSE0HMUcgu2xZymu9sdQVkxMz1TW/OHvxFv2eoiC9a1sRO2XwfaR4xn36StUvvMm+XVV+JWV061XT5ZNuZCRlkr8P32HElOdNueSMuL79+KFv1/I5F11RE7/iLVdIaSwlNFD+vLVX27mltUW/JbugR7XUrCuEf/sDSTUWKhPsGOMCKXm9efY1MdM+IZtjLFYsMVGYwsKoeGJZzhoNSGAriifsMSuPPL469w151tGfvUB5enD2HnPvxl700W4gsPZO2IAjinnEjZvNkXjRjKhvhEMBoiJ0RLbPfccZGUxfPcO5B92nNdcijo0HZbNQbdzH34WCxXRJsLDQih76VneufhMLj58gB/efYzHvvo/9r47PIpqf/+dme2bbemVDikkAULviA0LKmLXey1XL4q9F7ArqFgpIhYEVEAUBCmC1BA6UkMnpPe6Jdtn5vP742waWRS8er1+f/s+zz7ZnZlTZnY2c87nvJ/3nQ3O6YLyunFwdEhA7ZiRMOWsg2POG+j8wCRsWbMYAkmorLfj5oxIkOyHIiwSESY95AF9gfXZzaTS8GcfR+mad6GwJDVHjcjvg7B1FeR+NyLrmUd++36/5hokvvAAYDSw9OKHHmLk0h49gJEjcfKeaeifqWxHAq3v1RtxW7YAb77J0r9vuontiIrC8defwMD+VwNDh7YQmltj7Fhg3ryg3REcDvhNJgjn68QbjGh7FiIiIlBbW4uoqKjfru+/iQsZ1RQXF1N0dDQdOnSoeVvryMeKFSuoW7du5HA4mvcjFPkIIYQ/Hf9xiu1/iGOLp9GeKT1p0rvXkTOrJ0kKBeMk6PUs+qFUMo5G31F0dNM6NivUalnhlBQihYKOrQ3OufBFh5PbbQ/Khfj68PGgZUSlkvwjhrWfgY4fTxt3BWaxTam2TZyP3WuDC2DxPNX27s1mrGp1G1GuqvQB7dswmYg4jiSeZ9vPEvJyZ/QIKv4lq1Tk6JseVPyrOnNgcMGwhATK3bk56LVpt52I/Ju3kKhQUG5mX6pPTSUKC2P7cnOJkpNJVCpJbkr57N6daNUqckTEsPbOEhnbsnlde65BoO1tu2cQlZSQpNPRB+++Rb8MGEpPvv8Giwrp9URxcSTzPDke60yS30Vyv35U8cs2kkVfu3vLnrefbD8tahexODs64N62kmxfTCGp8QKeHQUF1Jg1iKRrr2s5l379ms9xz+r8oOnK5SNGsvfB0r+XBU9x9hsMwaMUTRGMfv1IBsjRpcv5pdwGieIEi5jIsvxfS7n90zgf+/btQ3V1NbKysqBQKKBQKJCdnY3p06dDoVBg/fr1OHPmDMxmc/N+ABg/fjxGjRoVtE61Wg2j0djmFUIIIfy9EJ+ahRNl0dBE2rCjQ2fIOj1L4xwzBrj5ZsYtMJng84n47tAPTCBLHTCTu/JKQBBgWbH6HPwNDqLkC7qPgKC8Bne4CeLG9UFFtpq5FWebkDXVdXb7KhX2fDQdeO89tpDUSpRrz3uL2rfRqxcQH4+dj09iM9GzhLwq33o6uPhXZCQKPw8uTLZrznfBy4giUq64js3M/X7g6ad/9XwUCgFCTAyOfrkcli++AFwu5rcyaBBw331wm8MhbdzU0tbKlW3riohgnAwEKAbn4BpQgFfDR0ZiVOE6VM27HWMzA4+bMWMgfToTpAb02yLAK7Tg7r4bsetzwAnKdnUZuvaBMWVQ242tvjupohD2z98ApzXAeM/z4Gvqz0sor6r6JxQVfQaO5yA3iaIRsboXLways5Hy9I3s2kyZAoClrbo9flgOHoRr4ABIAwfi0MP3Y82Jw1h9/BA8Gzei+yNvgPr1h/3S61C/aA3qF62B9cfNaIxNwLZnn8LJifejcPYsbF62BDt+Wol9WzagLrkH9j04AY09uqO2b9bvM6YLdj+D3e/0v8gHuZBRjd1up9zc3Davfv360R133EG5ublUUVHRbj8A+uijjyg/P/8PHzmFEEIIRKLVSs59+/7qblDhSy9R3oaNJEkSVb84h2x9BpL3mutIEiXyPPkseSyR5NabyTtgQHD+RmZ6cM6HxUT+QcF5DVV9soKWybtsOImS2H4GSkQbsle27XgrXkW7ugKRh7r0DBap6d69pcz48bRq3Zn2s98xY0i+fjwtWbOdiWL16tWmTMGy94POjH3jxtGe/auCzqZ/3FkcXDDMZGL8Gp4n6tSJ9e83rieNH0+L95WwrCOzmW1fuJDo/vsp7+IryO1rdd0KCshlsgTlQ1jTz81dsWW28HdWH/u4ZZ/F0ixA53zmqnbfzYVA9nnJsfBDalz6CcmS1O4ciehXs0AOHrqPaldMI5njSNRoSeY4qrr5X3Ri8RYqfHk6SQolVXRMo8bYJCodfgX9uDaPVq47Qzs/WkJ+hZJq+vUld1wsiUolNQ4eRL7u3UlWKKgucwBR585EN93U0lhODslKJUlDhwWPYKlURAMHElks5EqIZ9v+g2tzNv4XIx8XxPkwGAxIT09vs02v1yMiIqJ5e2xsbLtyHTp0QOfOnX/v+CiEEEL4FbiPHIG+f/+/uhvQxcQg6uLRAICo1/6NTbFKDJv0FBqzBkJ/+hg8M2bA9fX3aHhiBiw7VkP15XJYFSlQVJfD0nMA9k2YgC5n9kK7aD4a4rTgFTzUVdXQ9+6GopceQ0zOYai++RK2KDVUVXUIz8rErBenYPyxIzCtWYvS/oOh5DiEOVzouOcgvIP7QHMkD8WfvwZHxSbIsgSCDEetDWXbywCOA8dzECprYMwciPUP3YFOhXtgXvYpTncTIfACwiuOoS8R/H4JsiSjoVsaik7XQVPegB6bt6B/7vWQSk/hu6fewa55+xBXUoiJLje0e/ZgxKFcyMUFcI+9Gva8U1BWViF882ZE5v4CuXQS6j6dCl/ZHsggCJUViMnejO6nj0LOL0XF55+jsawMQlk5Om3ejP7HxkIqykPx9C/hOFELWZKhKK1Fh25pUBWchOOFp6HdtAW6TdkoyzuK8Kxe2HDnPUgrKIF+8bcoi4kDLwjQVJQjedMmDD92FeSCU/D27InK02eA/gMR/+qrSKiogLNfJhSn8/HLO69AXL8cg5wOVDV6oDOaUT/1PZwcdwsuttpgD4+CzqCF9/VJqPEXAwDUR08jztqAhmglyKxF7XsvoOi1S7H11MeMP+Ows5uFI2jf+wlY89PvutfcW5bBc/gAjP98FKQ3wu/1QJR8kCURckM1NF4X7OVnwJ04BqPXjbqiY5AaOWihhSzJkLx+RPkehf/MLohGM9xr18L4yx5Ev/kmohd/DmwTgM+jkT/1LQwZ3gf65GQk9DOzyI+uHE5zOKSN26A5egAYPhz6latYBs8tt2Df+wtxabKaZXPV1TXrjUhmC1yLl8IIXwsfpInXERXFoi19+0JVVQ2MHs0ypf4gGAwG2Gw2mEymP6zO/xQhhdMQQvibQ6ypAadS/dXdQK2tHtVffgFeqYSypgbDXnwZKq0WKhWARx6GfvMmaLdvhEW8G4LHDdhsMLz/CkA+cDs3oF99HbQqCWS1QvPSW5CMJuDUNoTlHEL6s+9DECWQzQbj5GcgFZVCtTkHj1bdBTWvA2e3ofjBR2C1WCAWFKGH0w2fQgEpKhwRi9cBl18CRXEF4i65HZ3jEhGmfB/+51+CNPoycNVHodm2HqNqimEQCJytAfljr0XSwL7Q23LBiSI8eg5iXBzMO7NR73eA1yrg6dUH5n274TWaMPa9Sch48knwETr47v8nwu68H5pII6SYaMgOBxrraqHKPYhwux3OSAPUEWZoFy2HY0gGU3ItKgTnaIRHioAmPByaBV+hom8/GEqKwTscoCg/pPBIhC9dCO+Ii8GrBOjsNTAc3AvIEtQvvw643JA7JMHsFaHbshUDK8oQrlCDs9khP/s4JLMRytPHwTtsMCgIotkMRWkpJFGCorQEisJCgGSEKzTgHnkUg8qswFVXwWcxIubgL0BpKQzJyej41CNATDSKpr+HpJ4ZUCYnI2zQOPaQNW0DYr7BkY+m4KqU0TAlJ+OBbuvYvuptQMRCYNgw7JjYE4PHTIH3y9fgiT4BfcrtUGgifvX+AoDq6qM4fHIFwkoBvkMisGUpwPPgBQU4QQFBUEBVU4ceOduhHTMW2rwClL41Ga6qEuTmNmB4Rm9wHAdBo4LarIeyYzR4pRIuwQTjqFHAs8+2Ty1vvXQVGCzwHIc6pxcxdjugUrF98fFtyzQtBT73HJCYCLFXH4geL+CxscFG08CjdXpy375wFxYg7NXXzkkg/T2Ijo5Gfn7+/9TgI+TtEkIIf3P81WTTJrhrqqlgyWL2oaCAKgcOZOHvptB3Sgr5IqPJave0CFBNndpMUjy9trDtdiJGHo2KImdpcdAyB/YtD1rGHRPB0gvPFiAbP76FiHmWyNiKHevIV5RPnhgzHbn9Nvruu++o9uUPiTQamnnsZ1ZGpSKaPLl5eaXh4sGsjtbpk4H6CtZ8FpQIunLD/KAkWSk+jn7OWXJBwmQUGUmiQknUv3+LZ0qgzNLtq9sKfUVGEvXuTbKCp5N3XkTetUvaibO5hmaRVHCmmXgpr1xJ3qhw1l5uLlFSUrOIWc7aNS3bWl17SkigVevnN+8T33yT9u7dS+7164liYkgcdw0dWP0a61dCAkllBWQ/MIuse98mx4b3WdrrWT4nPq+TNu14lw4c/uq3b8RzCOWt3pLd/tjAcognow9bJnrooTZiYrYewdOvvZYIsvcdyK6f0ciWpQLv63v2DVpGsoSTp3cfkvV6cn7/NdVVnqTKogNUvOl7Ei0mcqd2I5nn6cBrk1mZcxBIfy/+G0svIW+XEEL4/wT+6moIkZF/dTcAAJrIKPjt9vY7moh8t90G8auFkGW5RYDqueeYMNegQWgW/zprVij26gXIImBztivDgWtfJgBJ9kPRetbaOuWxNVEyMPO0+q1Q2BrBJXZFxLvv4brISChid8E+W4dobTgrExMD6PXtz/Fc6ZOtZ8DDhrFtZ4t/NfWb0HI+Z5WhJjG1wHb7hEdw6qgPqV26gj9TCO3cucA11wDV1ax/wYS+zpwBHnsM3A03IH5bPlRPpba003xtCLA3smtzzz2g7C3g/GJbuf0ePYClS5HxxFNAUcC7pmmGnpgIeL0Y/NgbQGE5sGwZ+EsvhXfHDpx0OtHL5wOfvQ0Z69YCP6wE8vLAL1gMQ+Aa+A9nQ8yMg+vtK6GiLtBOfBEHExvQ4CjDsKz7oVSHtb/Gv4ZW0ujeBhfO7D8Nv8cPSZQg+vzgS+oQ12sQyoddik79CqA4dQrOtDTwX3+NsKQkfHPLUHTzahHxzWfYZfIBAIzl1eiT1hnfP/wCrtq2Dd1XLMHq12cifd5spH/1OTifBNkQDnHKdHgEJrnP5+6F3uGAz28HZ9JD+vQT2FNjISg1WGUFrkxLR9LRo4BOh7SPZgL9h7YQSM8n5fbviD99KHSBCEU+Qgjh/GHL3kKNjVaqt1dTZX0JFVWfppMVuXSs/CCdqMilExW5dKrqGOVVn6AzNScpv/Y0FdXmUXFdPpXUF1JZQxGV20qp0lZGVfYKqnFUUa2zhupdddTgbiCrx0oOr4OcPic5fU5y+93kFb3kk3zkl/wkyVIbAaPTX37B3hQUkMdkIikgQ91EVPRbIsg38ByCYSnnmjFaSOzXN3iZXueYmYabSBpyjnbOJmIGttcEI5wGRLkq0rq1d2MtKCC/2XhOMTN3Wreg2+t6dg9+nhER1JCRElwwLK2lz0ePHKcXJzxEH916B7mVChb5aCrToQPRww+3P5+maEBBAZHZTKJG3badgLOspFWTzHEkaZTk69eVvN1iqSK5K3237yDtf+pp8qs1VJueTl6DgVbe/S/y6XTkjI6m+uQU2v7ONFq3+FsqGzmSPp8zgYpfu5Oq/3kJHd49nQ7vnkEnlr9G9iE9yWvSkJTcgyg2NrjDayBq4fp5DpV1MdDaI9+0v/F/zQn27BTU114j0uupumcP8mZkknXBImqoqientZG8GzaSrFKROy0taIpzXUb3oARRSamg+n6Z7H5oSlfOySFSKqngh5zg8vlxsVR/Yl+bfR5Joi9z9lDpqItJDvTZ2b1jWxGyPwhFRUXkdgcXZfujEIp8hBDC/yfYqi+DZXcRlLwKakEFFa+EilNCyykgkwyZZBAQeE+BzwQC+0wU+ERym20yEWQwnVK2h0CBugKlIQciB2wrw+nqHHQ0PQZnpB5hsh9H77sCqvwMdLt2LJydE2Gw1sFeUwqVyQD7O2/gmJlgOpiL3jYryuPjEWeOQN20GdgVmQYAiDiwH6MaG1ElibBYjHC8NxXH4tQw7T+M3jYrKuISEGUKR9W06Vgbzoy94g7sxXV2J7wNVVAaNPC//jwaxDNQHs1DpNUKzhjexgyuyQhOEx4Ht0mDxneewynzKQCA4WAe0m0N4KONENVKlEVG4uDjT2Lkww9DW1XFhLtqqyBfcxm4+XMgqZxA7jEorVbYzRpIBh1sU17EfqES4bmnMNBqhduogMughf2N53GECgEAptzTyLJZ4TUb4DaEofH113Ba8iIs9wjSrVb4jGFwGQ1onPomKic9h2ipCuaGOqhkgk+jg99gROOb78N+zR3QnTmFaKsNfksEGo06NLw1GUcm3ITRG9bBc/FuGK1WWGNioCfA8fLrOF3ng+HEEfQwW5D92BO4jERwb70Ffm8evIu/Qcxt/8ANTzwKNDQASgUiFiwAJk/GwJ07oLziCiiLiqCbOxdD7rkH+OAD0M6duMlRCwOvBWxWRE27CYgIh5x/BgjbCl8/DuLSFVCNGAc8/3x7B9eAsaH24EHkTb8birAYzDgwA1pBi5tSboJRFZBjOJcT7NlCef/4B3DoEHbffw2uThkN1bhxwD8CkR61ChQZifJJz6PLiFEtRNBhw4CoKOyf9iQu6XlVG4Ko1++FNQz45O6bcLlPj0EvvNBSRqeDYfX3QLd720e2wEH2+9tEsNTPPYe7EqMATUuKsW14P+jW7foP/isER1xcHEpLS/93kj/+1GHQ70Ao8hFCCOePrSVb/+ouNMPr89CKhW8QjR9Px76dRnTllS0mXRYL0apV5Ik0kS8rMyivYcbJnUFnjJ64ONpYawta5sNDxUHLeCMiyVFRHbRM7Zcrg253b9pwTiO2yjlPk2dQL7YvJ4coI4NozBhqHHpRW3GqVvX9sn1r0HZ2LZt9jplxHB388cegZUq3tOWJSE4rOZfMY1yZ7OA8kW2rW9UVEM2inBwii4WO3nhH2zJLlhBxHDX0ymQpuwDRkCEkdezIxNKImBGdRsNeFgv5VComkd+9O1F0NFG3bufm4jTdAxYLSUYt0bx5rG2Vqr18eSu+RtG/L2u+v+xeO312+DOasX8Gbc1Z0HJckHTqNgjUubKJh3JWurJ8/fUsYhckxXn92k/alak/tJ9yBybTjPnT6fh337UtExlJ7vQ+54yg+XoHkbxvVZaGDiVRqzl/c7kLxJ/N+/jTjeVCCCGEvx7ljeWI0/8BVtmFheclyvRbWLVrMfp2G9aywe9v4VV07AisXAmO8OtmcOcQEtML/LlN34IazgGCUhHcwE0jXFD7AMCrTVCfLGZmeRdf3NZevbU41fn07VztcBxU/G/0LVAXrzNBF9f1N/tdPutLyCZTszAYAECnQ+zBX9j3flYZTpKA8nL2oawMXFUVSKNmx15xBeDxAD4f0NAAd1QUux5KJeBwADwf3FiuaeY/ejSwfz9zpJs4kdWl1TLp9vHj2993t94Ky44TzdknBpUB92bci4f6PASz2gz3xnWo6NMV8qCBzSJg50R2NoY89kYbwbDmc966FQlTprbZR6II2rIFfR6bAnnQAJQ/ezeOVW7FgdK1KLDvRo9T5Rg/5S30+McdKLtlDPKyv0PhrtVoTOuKnEdeRN6jb6L8yx+wZ2MB9mwswMHtJWhI7YdPXrwRB165CWe+ew9rjs/GmuOzsXnDG/D26gmv5IHVVoLT40azTJf/6/hTh0G/A6HIRwghnB+2lmz9YwyjzlOU6ddQXlNE32XPbZ7BOdK7thOlor59SQbIn9I9KK+hLCsz6IzREx5BzkHB+RtlvfsFLeMzGEkaODBoGV9ycPM26VdEs7zdOjATtnXrWAbFAw8EeB3B+0yRkeTIDGI6FxlJ9pSu55wZN6amBi3jSQ/OE6HISHKdba4X4G/4dTqSOY7cd97ZlvMRGUn22Hgmed+6TEICbVn/I4n33E4yz5N4fD9JGjXJAMk6HRHHsUiHyUTE81SZksoiJBzH6gKIkpOJBIFknmfCZz16sKhGgEMhpnVlomhNx6tULHLC80QGQ7PUfhOXo/S2EcGNDQPn4/V7KWfaRNp79yU04/BCemfNInJZzFQyoA9VpqfQ8s/epy83LqWC4YPIYTSSM7EDec3hdPylt2jX8k105M2PSFIoyNMxmqTocPIN6kON018iWa8j2RhGzo5mkjQq8sZHk7t3KtV8N4ca1n1HskpF3r5d2oqJBc6xoVfmOYXESvukt9/XZEYYyAyrT+32h2e6NOHMmTPk9/v/8HqbEOJ8hBDC/wfgOK5FKvyPwu+w5i6or8IX389Av/jLsD5vB3r2SMXpex5A78rjUCxcAPf1YxGW2ROuW8bD8sAB+Ooa4DUaYXvrLRzUaKCtqEBKWk+88/QLuOnwKZgX/YjcyDQW8SivQFJGX6yY8TIGrd+AyKVfY2cnIwylFRjaNwOTH5qER3bvhXn+tzikigEA6CvLkNqlB/KfvwUJO4/AMPdDFHKl0FbWo0tKJxwYczXSrSLUn81HrU8FRVUlonqkIf+Gm5DUcATKBdNhV1dCpEaoKl0w9+yEQxelo8e3u1G7YAGOTZiAi9atg6tHDxz658MYXHsMioXz4QzXACAoDubCYrPBbzHBE6aF87UXkO8rhaamHonJnXFkzOXo61CB//wbeCWmu8BVlUPTIx0//3M8utSVImzR5zgZLsK0PxeDbFY4osMgW4ywvz8VR+OYLL22rAJde6dhx+PDkZVrg2bJLFTF1UObdxwdo8xY/trDyDpcg8RFi7Ht1puB+/4FdfZmdE5JxqQbn8ONNXmIXLAM+3Xd0H3rYQwrK8PAa28G72IGfTTuWvA+H3xaDdZ8/BmuufcfgMCBRB8EIkSfOM44QBwHzu8HAMiFBXB16wB1aQUqH5sA3ZET0D32EMon3okusgyv2wUtAAocz/l8wOnT7EZyOFiERRSBSy4BLr4YccOHAWUcsHQp8Prr7fghKoUK/R6aAm+fnii76SQqFKko690D3TbtBkpLce24ccB33wF6M0r6D0PShtVAr15I+XEx8OqzQIQSiImB9YvpiEkeAD45GcqPvwCG7wH0evivTYTu0QVQxXcA5s6FJsBrQVQUHHMfRkT4DS1iYgAQFYWDH0zDqO5pQYXEVsyeiokJvdvva/X741pH0f7gTJe4uDhUVFQgKSnpD633d+FPGwL9ToQiHyGEcH7IKc35Yyo6T2vuYMitLKT3ti1ricA02YpnDiQpow9JnbuSvG9fM9/AHxlL/sw+RIcPt+UoqFRkzwhuniYpleQdOiRoNkJVVmo7Yzc5PJw8ZiP5s/qQtGY1yf36kreqnDybN5CsUlFV7x5B66rv2TbjIX/pS5Q35zqSlQqq7pnOOAtNZZ54gkgQqGZg73Z9lrduITkhnkp/nEG+M6dJ1unIW1ZMng3r2Iw5JZPkPlkkd+1K/oJC8tsc5P+J7bP2yiSxbxaJ3bqSq7yEPJs2kBwfTx8d39aeJxKY/f9QsI19Potzsm/fUsZXOJtbolJRcfde5OudRWK3bmQtKqeauV+RT6WmFSdXET36KItmTJhAsiCQpBDYZ4BFLQKv5vcaDTPc4ziiqCiisDDy6DQkRUSQrNWRrNWS54knSOY48qamBa2rOYKi1bIoiEpFZDSSJPAk9+tLtGBBewM7lYrkkSOpITmepPvvpxP3X047l02i0lEB6/pWEvE0ZgwVX3wl2z5pUrOFfTOnZ8PSFs2SZ55pjkTYPp7AJOhnzWLXpW/fFu7QoQ/a6pwEvo/Nm9YFl8IfP55m7VoZfF+r39/2D18+79/f78GfyfsIRT5CCOH/OPyyHwIn/HEVnoc199n4pTQPe8pO4Ymh49ruiIrCjsc/QN9iG8Kn3wNu4WLA6QSuvhqCywVSK5lpVhPfIJBZcPKtReiXqWw3K/SbzXD/vAaqeke7bITNs1/GzfFD29iXcwMGoF60Q//xZzDedidw9z1QzZ3fXGbbR8/j+i6XtKvr+LS3MCSjDzB0KPyzPsEpbiA67v8ZolmHrQu+wfjo8JYy48YB336L7Ytn4lpFxzZ95iqrgfIKRL4wHUr6BNDroVrwTXM7zjdmwjKwMzB0KBSLF7HzDNOBIiNxePo0DO/CZs3a+V8DXbsCFRUYf+fDgNPHNDzOmhE3x76aZsvDhgE1NehxzyQgrwS45542ZbioKBR+OB+FjW70fWg8yl/9AHHDekHw+zDo9mcBm4/xMsaOBeZ+AU6SWVaUVguSRPA+FrVw3TAY+u93Mu5GEwQBaGyEkuMAjw8ygLpho2BY+C0AwO6sRkTryJpG01KeiJnZqVRsmyiiMasb+EVzEXbdbezYJiQmAmPHYuOTAzDIdBH4Oyciee9eoLAQvkMz2+qSdOoEzJ6NqD59QcOGgWva3lSP1wvLo5OAwpIWHZP+/YHkZBge/xHQ6ICFC1vqC5Qx3jkdOD25ReeksBDIzkbvE8dBhUVwfLUYzmonvH4JYpEVnTZtweXHDkAsrMC2919H0f4N8Eo+aEsrMCSjB9ZNfQ4xq79HUk4OVgxcAQIx3ZezcPb2ps8UyDoLVqb1zSJKIrp3737uY/5LCA0+Qgjhb4h8az66mrv+9oEXilaiTE2eFMHQ4G5EduEhPDlsfNsdAcKhxqCHZbQS3NJYcO++w/4xP/AAaNdeyJGR4FeuBH76qeWf9rlIiomJsPfMgErAOYTJgmzX6/HL5Im4uEks68or2cNk+XLA5ULM7lwgvN+v1iVMeh7dDtfCbXgGCuGb4O2cq89xcYyE6fMDpWVAQsJvl0lMBAYPgl6hb7tv2zZAqQQnSkBBQUtdQHNKKRVuZ58nTmR/CwuBsWNx6oXbkHXSCxw+3E7KW6HkMDgtDOgQh66zp+LU1DnoSQAcXqC4hPV/5UqIkRE4fu1gdFu4FoLLj/Iu3dDx9ElwRFD8dLDle1epGBG1sjLQXxGc2Qw4nYjWKgGdGlCrUTvpTWhefgH6yjr2iGw9cAEAWWbbFAogKQlhRwohjb8FOH6S3S8BkCyjpvYEend5GWE1XPNSYW3hMXjSumLPZ/MQu2oFTEu+x4G4JOjLytAxvQ/yp3yOzptWwfj1Chw3pEJbUY6e6f1hHZeBbkUV4Oa8g6rBIxCZ2g32l5+Af+4HiNx2Gnnv3w3jut3QfPMhHEMz0MnWAE+4FpxBD/+UV2EXGsFXVsPYOQnaY3lwRMRCde89KHjpfThHXgxLUTH4Rgc0XAfI0SIG/rwNWXc+CK1SCYVnB7jtv2Dis++isqoUsRIPdB/2q7+//wTby7ZDkiUI/B84efk9+NPiL78ToWWXEEL4beSU5vwxZFOi9qJMs2YFJ/m1wjvrF9PRPYfo9C9HKG//Mcrbf4zOHDhORas3kGixUENyb5J0eqr7ZhnVVDnIdvA4+S+9nFyjryL7zzsZUS8ge91MgkwLLjLmNZvJP2RQUCJmdWZycFGuXiltxM2ayZYWC/m06qBlbGmpJOv15F25kkRRZNe3SYCsV++W+lqVqel7DifchAQqWzWL6O23mZhWqzL+lCDpluciqQbqevv4zrZ1tULzskvrusaMIZ/ZwIibOh3Rt9+2acea2rtdO3J8PK38fhGdeeo68kQa6R/PdyavgqO8TkaSAZIAEjmQRykwEmrrFweSBZ6kaBOJ4WGMWKzVk6jSkAzQjptGkV/gSOK59mUBRlBtvQSTlMSIqwJPkk7D7pXA/eL2NdKPn40h0WJot1RYvugdEq+7lmxuD7l9fpJbLUWJSiV5hw0LuuRW26tP8/b8qZ823y++MAMjGvfuzYiz/foRrVrFUqaXb2ov3z9mDNUNHcOuaRDJ/ZVrz5wznZuI6LPlc4ji49n+YAJqfwAqGivoRN2JP7TOJoSWXUII4f8D/GFk07NFmZpm0L+CbpV50MdmgWQ246RWoXRPajo0hw/BHxsPw6P3o/DlD9CY2BkZO3dA09gIbPkJrrhEcF8tRG7/S6EtLkBPqw1uixPKMCOcr03FGTczygs7mosUhwOuxkqoTBq43n4WZcYT0B46g062BqjieHgterjffxklcfVQltUgPjkB+iOnUBcdAe3tt2Dn4/9CZ0cVDm1bhmsdDkChgN9ggu+Nd1ArmaE6ehCxVhsQngCXyQLrBzOwxxgPkmWEHz6A4TYbaiISoTS3iJnFHdiL6202VPk6QBtuQsP0t7GvB5Ndjz20GQPKyhB14xMgjw9kNsL12VTwZVXQWhvAmQSQMQzy1FeZKBnQLEzmNqrBBYTJDghViM3ejayyMjzWeyTI54cvPBwHZ3wMl86EeqcGV738b/TpoIdD5lD46Fg0jOgJdWktujuLYe/RFRWTZiBh4aeIefBhHLCkwpC7H6lWGxwRHaEwRcD69gzsM6QiNjsX/cvLcck/74XG4wZiY/FEt3/BrXsNSdU+kEYDvyTDrxYgagR4PALciemIPbUTJPDgunUH8vPB9RoAWr8eAKDwuoHZsyE//zx6H6mC12CAu2cMVCWNCCutBC8TvCOGQbV1GxOz49iSgT/SCGVpKTitFvB6IGvV4OfNAwwGWCeMR/bRb3HNM7tBkNkyzUMPNS8Vav1a8KtWwTjmcrbUN2BA81KUGB6OshU/oovL2W7JbddHs3BV1yRg6FAYd2wGAj6Nzu6pMJOPEV1ffJEtba1c2fJDaJ1K3Vq+Hzg/yf2A6VxTRIzqHaDwcHCDBwcXUPsDEKuPxemG00gOT/7D6vxd+FOGP/8BQpGPEEL4bWwr3fbbB/2JyN40nxzWmvY7msh9A4azz02zv4ICopEjyRUVTY1ffEH04IPtTOK2rV0TdFYoRoaTp748uGDY4Q+Dzz6vGEYNPj9rPzWVEfoyMogAOj7j66B17d6Yf85Z6fSNp85f5GzJEiKNhjbtW000YwYzHmt1nrTlnXO2k7/qs/biXxoNHf1qHtGMGSQbjVSbkkonR15DJ+5fSPL119P0ud/QxiXfUUNyMq3J3kqbF39LHoOBXGFGNnOePbuF4Bho59ttRUHbmb75Oyq492byaTS0f0hfsoaFkSM1jeoG9aEai4WcYWHkV7DIh0/F0nBFk5FoyBAWwWhKzQUYcZTjiN54g6TkHuQXOLIaVFSaYCApEC0RVao2ERBJqSYxPpZFTtQC+aMjaG/hd0SZmeSJjaBjj48j8Uwe0Zgx5Lkks73AW0EBuUYEiKmt08aXLGF9NRoZQTYuruV+HT+eVm3Z0UwEPbF4S/O+usEj2PuzBcgiI8memh48zdsYfk7J/bpzRPeaCKd+rZZKpr97/gJqvxN/ljhhSGQshBBC+FMxYND12Lnzu3Pu52Qvzpw6AuuIYRArq1Brd4G0WtT37AXcfDOwZQtgsQRNNWwvlkXguODiW3xZHZCZCUgS0KsXk+YGIANQ8zybfdbXM0Ltxx8DFguMv+wMWlejRzy3KNg5+iaKfqzcsx9OowkHXp+CeYeO4duGRriMRvjdxITbRBG4777zOE/gTN4ZHFjxAzx6HQqffByHC/Lg1elQml8Om0sAPF6UDBiFF0ZcjU/jvkZpcTEGxwzG6NQUmMPCcMWI4Rg1cADUQ4dCwSsYcfTBB9sJa+l3bcSBH9fAo9ej8MmncbiwGF69Hn22n0GnRyeDlwl9Zn4GLj0Lbq0eUBvgH3sVlEoefHw8IPA4PedReMMt4J1OIEIC1AqQKAICi8gRx0EWBNDkyZDLyyFIhDCniOgqT4ASyUGCAElQwRsdB+IFyOGRsL37DfzDL4agM8GVHIe8ovU4MSASHDikvr+sLVfhtwTeWpNbOQ7OHoHZfn09u09kGZSdjUEP/hs0cCBcjz+GxgY7io8Uo+RYMfS5B+DMSIfUvz9OXXs1dv+wBPt/Wom6bl2Qc8fdqHn4Edjmfominzeg6OcNKN26DbWdemDli/Ox76E3cPqLZVi57gy2fb8LktWGar8MmzEcp6Z8iBmbTmP6xtP4fsVO+K02lLpFWM1mmFataZaYDyaKFhQXKBRIrSwR/jL8KcOf/wChyEcIIfw2/urIBxHR+rVzqKGuvP2OggKSDFry9etDkk5L1R+/TSU/fEa2/r3IH6YmcUB/lkp5zz3NxzPORXBjN9FkIGnQgPYzSYuFZIGlbVKPHi3pmAUF5A03ktw0+5w/v43AlqjVBRf/Sj/3rLQsPSs45yQtoy0XhKiZR9As/nWWTTuldzpnO+3ExAJ1OdN7NddVdf3d9Mvs5bTy0efIZzS358q05h7k5gbl17h7pAVvp2MiiVo15aan0OFHJ9JX8+aRaDKR1DuTZLWSfK/eTtUvX0qygSffez2oftlckjRqsr/yDDOkMxmbhckko4HcN1xzFjeEo4JBw8gvCFRrMlBj507kTs0gf2Q0yTxPMs+TLy6J7K+9T76kziTpDeRO60KyXkfeFQvJ46wm7/GDJEdGkK9XR3YOPy4lctYRNdYSHdlLkklLjb17kKhV05nZz9GhQwup5Pm7SAboRN++5IiIIEkQ6P2CClq8ZDmJShUV9UgmX0wMNQ4fToXZOVR05AxVf/0dSQoFubL6kNixI/nHjSPJ72/1/fY4p+mcPGJEe25JQgJ9tOFU0KiXIyqGREkiX2ER+dRKoquvZvsWLmz57n4NFygUuKFww2/X+TsQ4nyEEML/cfwvzFxGX3ovlv0wBVePvAdlez+BWsek3oWKOui7RkOXdwa+mAiYX5yKiofvhO7EGRAE+Kur4RozBlxdHWq+/RaK6mpEd+mK3LFD0bPRCsUX76OR8lldlXXQdooCXpwE1c7d4Od/CVElgKushKJ7N/h91RBEgpgeBeWrTwNqFRwn5kJMj8LL78/CVRvXInrFMnStKUTdxkXoarNCDjeBc/vgm/YW7OEaKA4cgsVmBR8VC48xHM4pH+GMgqUihuXuRYrVBskCOA3hsL3xEfZTV2gqS5GW0hfLbp2IS4u3Q/fF5ygiCQCgrqxC5wH9seW+EUgvKoc+7wSK/tED6oPz0DkjAbXjO6JLvgncnHcguo4BALjqeii7J2DfLRchvFwF/Refo5gkqCur0GlAf2y/5REMLauEMv8E3Pe9CEN5Hbi4EXBkHMR3t96PAVX7YFywACfVGoQfPoyBa9fCxAugzF448s9HkbBsNbb1Gw+Fy4nMlL6oSMtAsr8eivfeg/PMGfB1dQjr2RNr7xiCS4wp6Lj/EBpenYzkxR+AdzkBrQ6IiILyzSWI7NYVcMjg3q2BueIeeM0m6D/4hEUZOAB+kZ2T3QH19z+y9FuJXRsQoeOubbBGa2F0+iHU1YHnGgCHA96eveB1eXGqax+kLJiD2l59ELtmOZxyZygtFtDHc+Ho1gnCvn0w22zgIpWASQN8+AYQVQWAAw7ng3N6oTPGgIv1o8vmfOD+qUBOPQjz0PXkcSgaXYBSiccNSiAuAoiOwv7PPsN1XTpDmZwMfc9UFv1qKIcYbkHN/C/RwRzBxMRsNnYeUVGofOtpdO41pl1quGQ2wbdqBfS2xrbckia0jno1pT9zHGw+CeEOOxqNBigLC9mx55l91ga/IRR4qOYQMqIyzq+uPxN/yvDnP0Ao8hFCCL+NP0xg7D9EeeUBWrTudnI3lLVsLCigmpH9WmZiKSls/d9iIb/BQI2vvc4ktA0GxgeZNo1Jb6d2DTqTlJUKkoNlKSiVJBsMrC61mrUxfz5ReDjJRi05eyZT2VuTmVDW4EFM+jsigmz73g/K3yjbveKcXIzcH063L6NSka1nr3NKaddmpgTNrLD1DG7TTioVuZKTg5dJ73Pudnr1bl8mJoZOjxpB9MorjHfRrRvjmwSudW1KenChtYy2s/mSRe+SGBvF2tuxg2V+7N9PdOWV5OuSSDRgAOWPvYr8M2ewCJRSySIYADv2bCGxQASksk8XktQqkjUs86jqhn+SNb4Ta6eJ5xDIwNmwOT/o9+XdvCzo9yVFm0nyWtvumzmTZJ6n5QXbiG69lUXeWvFwfsjeytpt4iEF6hJjY+lo9sa2+wJRhvxVnwUVDHNcMopqKoraipxZLERKJXk1OsY5OSvq5baEkztgIbDz3clkv/bqlv3nkX12vkKBkiz9aVEPohDnI4QQQvgvIS6mN2K6jEED19hmu9R65nXxxUy3obER4Djo3nwDUKuBt95iLP4vvgCiouB+/l/ADz+wDIbW6/dmE+inVe33hYezvwYD05gYPhzYtQsYMABcn27Qrd2A+G9WgMvIAH/NtcBjjwF1ddD9exZrt0mwq6ICqKhAxIOT2HadrqWNwL6uL93RtgwAREXh0KTXgvc5Kgqb3nms/b6oKBx//Ylzlil77pmgZU68+fE5y+x9bDz2vDQWjdYK1Lz1FEpKd0GUfIjbv5/JkhMBb77Jrk3gWu99Y3rQdja+dV+b7T6vjmXZbP4B/pyfQAoB4juvQa4qheQB6tQGmI7lYf2a3SAAXo0WEsceKxLPw6dSMQn2wEsO8EAsZ+pR36kzrH3SUPHA47BVlcFUXQoMHBic53AeZnutQVKLdb3nw/fgDzODI8KwGx8AfvyRcWHGj28WDBv+0IOs3dYCbomJ4EURSfcH2Zedjbhn3m7uK8kyJK8N/sZSaH/ZB83FYyAPGIDi2+/Gie0HYOvSA/X9BmLms1/AZY5CwZfLsH7jGazfeAY524tQ2iML+cvWAp99hgE1Pnz25v0tPI4lS9j3+FuGjyNHMl2Yzz47pzHdnso9GBA34Nfr+S8htOwSQggh/Ee4qNsdWH7kA1xn6sE2dOqEvDceQsz1T7Nw8/79LGR98iR8sbHQnjrFRKwmTgTWrQOmTQMsFmhf+QRwWtqJb1F6KntynS3y1acP5Po6CCYzkJcHDB4MrFkDdOgAvPZPoKGBPXi7dmVl5s9nDx1JBvLzgaSkFiEvhQKcKAP5BS3bASYYplCwZYOCVmUCgmFqnSG4YNigQdAI6qDCZBzHnbMMEOQ8f02YbNAgpMYPQsf4WIgJ38P51OPQ7doPwWqHTpSAo0dbrvWWLez9r7RjEsLabJc3/wQOPFQvvse+R40WitMlwMGjsD/2NqLnvwUYjbhs7SKQQgH1ZZeiwu5E7Pq1EEQRwtnhfyJ4VFpo9DrobQ5o09OBaa/D1rkLkJLC0kofe4w9PCdMAHw+ZD18I1Bwoq2SqNcLxcMvAAUljEj85JPs4dzQADR6IPbpBxRW4IdJl2J753JctvoXjFEKeP+ByRheUYn+n0+H45/3YvPzb2J4Zj9Mvf1p3HsyB6bPv0KuFAYOQPjRQ7jIaoMvRgmfSQv3O5NQai6BsrwWMRkdcfqlrkjerYbi23fgijsOXtBCkVuGMKcLHPyQIiIRtWEtXJNfglatgPKXPXikqgYKex0679uEzjPfZAOnbRWg/Vvhuf5KQPSCt9mguboPSEfgmpSHLyTl9hxLNX7ZD4/ogUFl+O06/gsIDT5CCOFvBofXibWnd6O4VgrEsmU0OdUDAT4IBWabRM38EGraFpQvQq3eEYgAPiAj0vSX6YoQeI4DOIADAeDAc8DR2nJwyh9gUDEJbEfNcVT36YmjH76FLrM+Q4eP5wIcoD19Gp70TGiO5kLu0xf88WOg1J7gykohebzgH3gAcloqkNUH/skvACmpUO8/DFx6OejIEYiffo6D3y1Cra8aI7dtgUrm2Sx9zRpg0iSmZjp9OlB0BJT/CuSZM8A//QxoyGBw+w8AFjPqProF4dlKKGZ9Ap+jFnBaoYoIR8WnkxG16gw0n8yEvd4FABDsHugjInH09YXofvBbhM2Zidp6FwS7B+FbspF85DiotAj+BQtabOVdLiizt2DI8UOgogpwTVLeAJCdjdSjB4GSJ1skvlvtSzh8CCivaLsvOxupx04DxWeClok5cQIoLIRi2TLExGQCvYzA0GHwHj0C9YsvAU8/zR7KgwY1P6gHHj0JlBS0a6fP0b3wF1Xh5/cmoOzwp5Ar9+PW9G7wvjcFmsXfQ7NkMewvPYXwW++B9vOpEEU/6h97CapjB2BcOB20bBliAjeixPPgm7geAGRBAV4SofJ5UJIyHNH5p4B33wWioqCQACosgGPOPMhKFRROB45ou6C/1QbR5GvWZamRzFAHdFlcZjM0xjBIH38ESu0G+4uPQpmzE+ZXD0NpiQPn4xC3fC+6/XsiBl99Kbhl2Xjm3ddh0msAJY+IVctwt8MBqICZT9wAbe9MYO9epN0wFrjpJsAgAzHROPzNLFwU0Req5GSYul4H+I4D219Cz+ds0EpmwGaD9p072IO+fhso8lM4vnwVsfEjoExOhvaN14Ajuex+Ki8GPv8MeOGFNpEUOSICOyc+hzClG5mPPoThX67F4musuLXp4l2o4WMQY7qd5TsxOH7w+dfxJyM0+AghhL8ZHG4eV3W8BV2jwsAFhJn4VoJjPM/cHZr2cVzgMzi27Sw33NZeEGd7RsgUGMAQ1zwoAXGQiVrC6TIwuvNQfH+6Fo/07QCO43DsaAmiDacR1TUDvnE3AHPmAzwPMTICmiOHAa0OnndnQrH6Ryi//QpiZh/szhyIoZ+9D9uAPmi44WrEPz0ZVY/chw5OFzxOgtIQDs/MT1F6fSkghkHt9sKv1YCLM0N8/1n4L+sGxalsqP1eOCBCo9NAed+/Iet1EK21EAf0hi5nNyLGfAjB64EcGYnGY3uhPHEIqopKJIy8F4LHA19ENPL35UM0W2A+mIfuFeVI/0cW1B4XvJHRyDteDF3eSYTb7fCYI6ELC4P3vXdRSSJIJmiOH0ei1QbZIsBl0cL2wfM4mHQGln15GGCzojFKDbVeD+/LL6OiwQoAUB8/hiSrFU6DGVyYEc7XpuCMW8VE1qw2WI0x4A3hsL35EfajGwDAkrsXg6w2NFpMUBh18L/1Iho0lVAeOonIbTlQSTLEZ56D4POhMTYRR+96EnHzlyLRaoPDpIE2TAvnq88jz5mPsGN5SLU2wBuhgjI2HldtqQQe+AgFZz6AcddccE+9BNTXQ66zITIpGbjiSkgH9qNMEwXzV+9hz53PYjTPw9G7H6QOBliWbwBHcuCm4pjzrSzBY7RAbW9AWG0ptIV5LIoxciSK+l6KtI9ehjHSwJZ9hg7FoPJ9QEQ4zDUlUKalQLl9M/SOQmBQGhATjWPvfYUBWYlQdO8OXHopNBePA8ocIEEB17c/IIwXMbh7dwy94SHg+HEgKgpHX5yCIcP7tPECAoCyzAHotmUtW5564gk2+GiN1ss7AWGyykWPorPyuvYOtRwP0W1rKbN4MTBkCKDXw5nUE2GbN7eLYHH9+qNvRDgMMWpwCQlI+2gWyha93pJy29qPJhh+QyjQ5XdBwSmgFtTnruO/jT+NefI7ESKchhDCryO31Eo1Ds9f3Y12mLYrv/n90ZWfkv+6a6m6row8G9aSrFWTpFWRGKYhmeMYSfSGGxgRL0BOlAEmyKXTMWJkgHToiYylmnoXUUkJyTodndjwNRV88SbJCQmUu3NzOzKiFB9HP6xdQEe+WsRku8eOJamoiNxJHUjmODqxfHNb8a+AwNbuDflttxM17/tob2FQwbBtq9aek6RatmpWUKLkvn1Lz1lm08rdQcssWZd3zjJLN+9tXyYykiSFkomsAewvESP/xsbS0u2rg7bzwS8ftNle8MOH5I9rIZxKGiVzfR0/nnL+8U9q7NiZbBm9KGfTbqqOiCVvYiLtGX89yQB5tSryKZXkUyhI5AWSOJ52f/IJNRqN5DUYWs5BpSJbtzR2bbt3b0M4bT6Pfv3YPRMR0SIKt2InOzY+npEshw4l0mpJNJjInl8elCS6fWFwV9nTwy9n74OIiTX0Tm0hcRYUEIWHs7TojA5tBdxalfGkdyFZq6Xql99uTn1ultbneaLvvmtTRg4iu1+2YQWVDslgx5xvyu05sLFoI4mS+LvLny9ChNMQQvg/DKvLD7NW+Vd3ox2SjBqcqGdy4f74GKx98UEcLzqDI5++Cfj84NQ68G4/fNePZTyKY8cYwTAtDejYEZJKDfj9jFj4zDNtSIckA7BawUVGInlvCTr1GNESozmLjOj1+5CS2gv5JTUQBQXqDh3F/O83w+6X4dPqYf1iIYpVPSB7/cjvNgalxYCoN6H4WD1KOneA7PehYEgmDu7dijqlAMlshtXlDi4YxnGQ6+uDkyGb+hZMtMxqBcxmoGNHVu999wEeD9Raxa+TK8+jnTNeL0pNJrh1OmwbcTUj0FZXA3V1wBVXAC7XOevTKXVt6pLio+Htl8l2Ggzwd4sFHngAyM5G9yN7oampRvnzL8G6dg3C66ugKCtDnxU/AgCkuI4QJAmcLIOXJbijzeC2f4yjvbpDJhnb1v2EQ7t2QZZlSD4PYLcDJSUthNPEREYkBrHIxeuvA253M0k09bkJ7NipUxkXYts24K23wPs80I4fCxo0CP4PP4Tn3/+Gu64OtHkzer74OOSBA1H/2ERU5B9F8ckDKDl1APEHd8CW1QvigAHY969b8dP+LdhweDvKM1Ox8N0rcfCVm3Dmu/ewOe9rVPeMhU/ywO53gR6ciAOPjMPGgp+wseAnbC/Zgup+yTh6579wZtBNENdmo3DWSvgqauDrkgzrP2+HeNmlkN54HZ7NG+Fd9A0oMxPw+8F5bS1S8YWFiL/xLkTk5jHRMIsF2LCBfYcXiDp3Hcxq819vJHc2/vSh0AUiFPkIIYRfx+YTVX91F4JCFCV6e+cZIiI6uG85fb1vM63/6huScg+Rq08y0ZgxJPbJJPfEe5i8OsexyIfFQvTGG+TT6Ni23r1ZKuRVVxFZLCQpFCTp9W1TFAMzzMbM9HYCWy6jkYqSu5JHpSbJyMTGRK2Ozjz1ApFKRTU9+7YV/wrMvt1pqSTrtOS44xayPv0EFR7dTRXzZpGsVJA9o3tQwTBbWs/2ImNNs9+0LkGFyRy9UtqKnxGxNFaFgqXuBjOwa+pzkHZqAgJk3mXf0uYFn9GyL78ll0pFIi+QV6Wik8+8ymbbHTqwCIjRSDWZaUHbKevVhSS9jmqXLqTSulLavWEm+S0G8vbLJEmnoZMfXkWnV0wj60V96cd1n9OpN2ZQ2Z0P0roP11FtxhCyjhpBO557mbxqgXaOSaNDAzrRirvupVNpfciv5Km2o4HcKhWtuvsWOnrsZyr8+gPyR1jIo9eRqNGQJAhUdvfdVH7Pv+jEvK9IUiiYLLpKSRLPU2NSHG1ZNouqBvUmr95Ivg7dyG8KJ3fPflS0sYhKv9lGPp2B9n04i8qffJLqb7iBzqxZQ2XT3iVZoSBnciL5EuOp8fJLqPDUYarM2UCS2USiSUe+3pnkfvwx8j31FPnzTpMciHA0pHdoMZXbv59FMcaPp0Ur5rQXcAuUcaRmkrx6DfmSM6nx523N26Xkri11tbIDoPHjqXHWq2x7RgaR2cxMEMO0LeJ555NyGwQbCjf8cSaUv4GQyFgIIfwfRoPL91d3ISgEgYcVhFdOlULv1aGbVI8GWUL2kTx0FfQovGcsht/8ENQHc5kRnUoN2GyQlWrwk1+ErFJD5jhwBw+ivlNXWNasQU33VJQn98WRe57HuJfuQuncZTjCdYWushSZKX2RPWEirizbBuW3n8MRK0Ioq4JhQAZ23nsbOm84gZSfVmDtpHeQtGU9oo4cQkn/IZhx48O4t/oEdPmnUXzdZTD8cgA9ZUKjCChMYRDK8mG/7wooa0+A07jhzUqF5ngePNEREBZ9g+JXHob3yM+I79Udx24chS77a6H+6CPUlZcCAJS1dYjo1BE77roK3W12aBd9gvLIRqjKq5HQqxtWPnonuhxzInr1T1DbHMjJPY64Q0cxXK2BUFwEe0QslLfcgV+eeguOpK7ITOmLd+99E2NKtyNy4XLsCE8GQDCUl2J4Zn98dffDuOrkfsR8MBnOG67Gqfru6JWYCMEQBsVNlyFy/y54unWC/bJhcPZNR4dHXsU7Uy/B5blOhH/7AfbGFiKsrBaDenfA+w8PxoQTRhjW/IiC1FhIh8rAN7ohKxSgqCh0zhbA9eoDxfZJuPSJWdBoBcBmQ/yj/4JnsQ8ajRGDP/kIUGoxMLsA3uRO2P3PJCDGhW5vH4Cs0kCOVqPjqWKcWlOAq1+eDMHpBB/gIUlJiYjcuwekUIC7aBhkowFw2EE9e0Jx4CCUmX2QlTwMDv4rlKT1wtYnZuGuV26CUHwSSTf3Bbp0BUk+9H7mcfCDBwN798Jy443AoIFATAysn76J+C6jmZhYeDygNACDBsMv1YKbOQea2yewm/m225gpnV6PAw9m4aJnAqZyixcDe/YAHIfxP62GtPQHCC++yDJLgOYyp+5+FlkvT4TykQlQbvixebt48+VQNRnUnWVGx7ncjFR6443A118DGRnYcW0fjHz7bUCjOS/Dx7NR4ihBgiHhjzOh/CPx54+FLgyhyEcIIfw6tp6qJr8o/dXdCIp6t48+OFTcZtuWbcuobMwoWjjvLWqMCqcdl95InjBTQILbTJJKzaIY0XFEMTFEAwYQ/etf7L1aTcWXXUVlS5e3FSabN49ozBjymsLb2sYHZpKiyUxy/wFM6GrmTCbApNcTmUx08okn2OfWImcJCZSdnc0iEADRkCFsthloxzUyIFfdZJQXiJZ4kzuxfmm1LbbrTZb2JmPQvrnNZrZdo2F9GDqUHdOvH50afZYde6CM03LWeRI196EwayBRv37k79yZ3nnoUVo45QOqGTCIvJZA+1ot0cCBrMz8+UQcR7X9uwcVGavKSm0rMrZgPvmjo1nZJi5IwFK+YE2An9KhA9Ezz5BkMAQk7wXKe38iVd12CUlqJUmiyOo3mShnwnV0+GA2eTQaWjB1HnkGDyOKiaGa3r3Z9zN4MFFMDMl6PclRUUQAiQJP1LMnu1aB79BlMJBLF0bZL39KNGkSu1cCEvqegYNIMppYn994g4mrNfFwsue354KMGUP+yweRb9sKxt+YNauZ10JEtHXF5LY8kECkIvuDl8n11JMtEYlWEucHF61vWyaw3Td/JslZWS0/jlbiYLJa1cIrCXBEbF0TWdTqHKJhv4X1het/V7nfiwt5focGHyGE8DdDfk0jFdQ0/tXdOCdmHCqmSicjxBbbK+j7MwEHzZwckmKiyLbhW6qI6x4gJepJ5jhqjEkkjzaMKCysnSKmJAgkqVREw4axf/TPPcdC02PGUOXwwMO66SFTUEDyiKHkyUoj/9Ej7KHV9GB67TWi5GRq7NyJfW6qKyWFKCGBfv75Z6K1a5laqk5HNGgQe3Cnp5OsVLAHeVxcG9Jj9bzXia68kj3Mnnuu2VuGxoyhmmFD2/WNxoyhkosvYduffJINQIiIPvqIKCmJ/Eolayc2lrUTKHPqkiva1hW4npSQQB8eLGoeBDS++BLZDucSjRlDVZcMbmmH55sJmRQRQWuOzW4ZOLQi0C7Zs6TN9tKvF7QMPpoe2oEHc8Ga3W1UPN2jR7K/6T3I/egDbcmbgfoP7ppNZ3JWkz8xgfaOG0d1/QaQd/Qo8urD2MCC56nmhcl0uvNwWn3bByQ3EZF1OjYgio9n31dBAfmNRna/nLWEZV2wiCSjsaXPTSqjwbxzmvx++vUkWadtt505zapp0byFdMzhajMwqBmUxXxs9HqSFQqSdDqSeZ683buQqNGQf8508u7LIe+yBSSbjOTs2YMktZqK376Lcnd+QLk7P6ATy16kkpG96ccDG+nE/XdSyQ030d4v5pFPp2eDc55j98JNN13w7/BE3QkqshVdcLn/BKHBRwgh/B+GX5Ro66nqv7ob54RflOi59d/QDwXb6IeCHeQVfWxHTg7JSiX501OaTcZknmdRjqgoqkvqyB6SraW5eZ5WbdlHvoQEJo9O1OaB0jz4aPWgkw/vI2/vFPI7XWx7enobi/LGTp3aWpZnZBBFRpI1PZ09nPsFpOGb9qWkkKxQtDy8e/Rons1Wz3ud6NJLWdtnPWwlRWDA0rMnkzcnIiooaBl8rF3L1vabkJJC/qZ2mh62gTLNg4+zszHGj2eDjyAZHFUXD2jfzkcfESUn0+pjHweta/HuxW22l2zYQKLF0la2u8mcLi2zzbamwYdoMZGs17FzaMfR6dZSJieH3QOtBxixsSSpVSTxPNljYtgDmGP3ASkUbCDJ80QDB5Jfo2HvU1MZpyKQLSVzHBvYhoWRxPG0/+HHaO28r0hUKMnWvRv5YmOpccRIKvx5PZUs+Iqc/fuTz6glb5co8kcZqeLLiVT27WMkKwWq6hNLjqRYOnX1NfRaXlnz4MM19iqavOIlOnb7HSSqVFRx2eV0ePpMkjmORI2KxA6JJI29iqTaCpKPHCL58svJb9azvsbGsghZ032iUpJtyMCWiNP+/UQjRhAlJNC6ac8xflTrLKfzgCzL//WoB1Fo8BFCCP/n8b9KOm3C4cKjtHH1d1R+aDWVH1xF5YdWU8HyWVQ3PI1+mfo8uaJjSQbI10QoVCqoPCGBfFoNyQA19ExpdkL9ZP5isnXpQbIgkCsziySdnkrnfkeFm/eTx2AhsU9fkrU68nw6n1xH88n9cw6JBhO5emeSpNNSxRuvk2QJJ2+fviRrNORVq0lWKEjU6UhSqUjU6clvtlBDUkcSVSrWLs+TqFSSX6slUa0mSeCp6OOXqfLJO8kfaaa8DXPIbzGSrXMkyQA5u3YiUaulgrcmk9/U8gBs+uuLjaPj1bWU98t+8ui0VN+9O0kaDXkG96cSeykV20rI9uB95Nbp6EhlMVW++Dz5oqPoeHUJ5f2yixxmC3kGDiJZqyXJaCTv0OHkS+tJksFIpRlZJOt05FiymKx1VWStq6Gdy14nr1lPjt5xJKpVJA7qRy5bGXkO7yFZEKi6ZxJJej1VrfieCh21VHJkP4kREVSRkkyiRktHp8ymHesO0dYPllBNn0F0YuVmKnv6Raq94TbK+2IR2YeMoIqpr5Pz3/+mutGDqXDhWrIPG0UnPn+DJIVA7ogI8ut1ZO/fn/LmzaWS558jWaGgqlQzNSZaqOqKLDry4b9I5jiy9epEfqWC/IKCZm3fTA3x8SQDVJuQcJYjLqjo7tEkcyBbzw4kAuTX6umdH9aTKyaWZEGgkys3U9GV17FBLRFLj46NJcrJIXdULNldPhbZ0WhYZGngQCKTiRoz0tnxrZfVEhIo++TH7VOcCwrIc+1Y+unwT2xgZzSywer8+UQKBXl7dGpbJjBgsV6U2bYNIqKcHPLFxdIPJbltlrBoxAiimBhyWUysHqWS6K67zvv3t79qP9W4av6Q3/KFIJRqG0IIIfylyOiYBreiN8I6jkZcr6sQl3kljtdIcCmNqO44COq4GAAAHxCi4v0ioh12KNweAIAhv7C5rn/d90/oyktAag2UVRWw3X4PdLu2Qagoh8rdCNntB4WHQ/huMWSPH1RcDN7jgsNtgz/CAmnZt/D3HwDH4qVwPfUcSG8COB5SSio4IkYEHDIMNGsOiA9w8DkOvChC8HjgeuEFAEDSxFcR9f4CcFYHkq56CCTKMBQ3QFQJ0OYXQTabkPjOLPg7JMKdwoTARI0GpFBAUVONBrcHjfkFUHp9AHngjzTDFWVGrbsajlOHoFuwCGqXC8ldkhH5znvw6rQoPnMCFccPQetsRKPTDb8pHJJWj9Nzl6L8/ifBOeyIEJ3wW0yQP/sElflHUJF/CB3L6qFsdEOAEW6dGv6TZ+CuLIcndz9ABL7OC1e4CZ45s7GnJg+nju0F7HaISh2EuFikHdyMwZdlIqujCRFH9iH53VcQv/knRBzai64xBhh+2YWwrxdCWL8chlOF6HhZPxhyNiM5eSTIEo7GHTugOHEShqNH0fXqa5B45VXgYmLQMPMR7PzsJoRvzkVPbw9wRDBqYgCOhyCJuOuhZ2D2esFxHCI++AAczx5RnE4HjuPR4ctN4MDBKOnAcxwUHheeevN5NCQlgSNCj6tHQXv5KECWWVrqqFEshffUKahrq6C+9OIW/564OEYg7dMH6iYX2bFjgZqa5nvPXBKBo1s2wWswoHTyJOzbsQ25+/aC37IVQ+94lkmeEwErVjASqSjCft8Nv54W3bqNxERwgwejyt2IIz98BwfPsVRmnY6lGZMMEkXg+uuBI0fO67cnk4x6Tz0itZHndfxfhv/CYOiCEIp8hBDCb+N/PfJBRCT6Jfph5YnmzznL5lLp6KF0x2vP0Om4Liw0rlSQT2AuqI2JkSRzLVwPsckddcAA8sXGs9llSQkLT/frR7RqFcmxsWTfcjCoYNb6tXOISkpIVKmIxo4NdCKHzaKVSrYckpBAEseTqFBQXdcUkjiOLa1068a4BQCRSsWWBgSB6NlniQYMIFGlpPqB/YlGjSJPmIbIZGKz2ZQUxkMI9F3mOJY+zHFt+ra8YFv72fGIEeSJMZOv4DRRly5El13Who/xy/atLc6ytbWMDKtUknfTOdxdY8LJmfM9uU+cIkmrbdP+T4fKgl6zFTuK22z3rNlA/shYVmfTzHzCBKKEBDq6+UBb3kigXTEqiqqOHWfLN0lJbc6hZMssotxc8sSFU/VFGez7HTq0OaXWFhlHolIV4DsIbSIfkoJ9lgSeKDOzxTm3d+8W4bq+fZuPJ5WK8XceeohowgSSlCpyDwjweDp3bll6W7uWfbdBlpa8vTKCpjhL142jM9uOsmWsLl1YXR99RKRSkT/KErSM36xv7zobaKc0syf5VSra+e+7yVpbTc68PPJcdhkdHj2AcmdNZUuTZ4mf0ciR7HfQtIQTwPay7dTo+2s4YaHIRwgh/H8AuhCvh78AgoJHWloUtizegrxtB3GsRom7+z+JcusoFPW8CgDAg6CQWPRDV1oLp1EPAiAqFeDlgDz3oUMQGurZTLDJ4+Luu4GVK1sa+xUxL9EYBhQUtNrYylwOzKNGUChgOXMCHAByu0H5Z+AbewmTj/f7AUmClJEBbNwIf00NRIEDp+SZ6BgBiI1ls9mLLwacTjbzbgIRS5Vs1TePpxx1Z1ZBsujR+MHTqKndAq/CCVt6JDsXiwWor28raAawWTwR0LcvM14zGs95/pyggQIaaPxe8BciWtaqLqFzR3jS+rRsb5IF/xWzO04UYb79NnbM55+zfQFhsKiH3mOGfPMWoeLKnswDAGi+Xq7EJPh0OnAcB374MHAGQ9M3Bl6UmLCcJEO22Vg5ngfKy+E1mZiB4IcfAoIASaOBp3tX+E1GVPAiDl4xAm6zEWve+gClEybCW2+Db8NmWDP6Q7rmWshEqHU3wmk2omzaFGw4vAPlmanIfvn1oC6xHAD+1GpURvshV1XAXX6GvVcp4NMIaLj/RjjmT0dZ9nyUZc9HzcpPwTvckHbvhOj3wnPjOKz5+BEs3zwTks0KL++CNyYSiWVF2Oo4hSMH1kK5eRN6bv0Fac+8Bsyd28anBSNHMqPAH34AXnyxebNX8kKUReiVevzP488fC10YQpGPEEL4bRyvsFG51fVXd+O88NH7c+jLN16luoo6euDrxbRszvu08bY7SQJIBNd2Xb8p6qFWs6hDgEjYZibbatYoh0eQP7130NlkQ1pXIr2ejn38Lh0/eZjtW7KEtaNQsvoAqu+eQhSQAZf4QH94RoT09uhKMs+3kCID3AOZ41h0BKCGDtGsr2lpLGKQkcFSTpsiH00z9KZ028hIqslKI1mvpcZln5DVdpjsh9eSbDGRx6RtES1rncIZGUmOjAw2ax82LHBhPyLSaklKTwk605YjI0nKCC4mVt+7f9Dt1el9SNLpqGj+PDp25jQd37yB/AYjSVm9SdZpyf/5x+Rbv4pki5nqe/QgUaulXz6eQit3r6Qfdv5A65bNoZKBfenkrCXkeOwlco27neqXbiXr7G/JO2gk5cx8kopeup/KbriCsm8Yw/gwen3L998q2iFqVCTpNK3uDY6kpn2BSEnT8aKSiZDl33EbuU0mcoVpaeecd8ivVpEkCNSY0iMQTeEZn0ehIFlQkH/YCJLi4sinVLPr0BT1CYiJ+c5O5SZi0TOVijw9upCvR1cW+UlPZ9cwLY0RjTt3blfGHW4mcdx1rI2m1OxARGj1sY/bR6JiYsh/3Vjasvm9lkhH03cVSCsmi4VF2gIRkM3Fm8nXRPD+CxCKfIQQwv9xdI7Uo6DG+Vd347zw0KP3wiposWHdTnT3+FHnKkKhsQEcAFHVIhNPAvt3RAB4r5dFHerr4Y2NhaTWoOTz7+G3RMD2+Tco/GkPSrcchKtrNzTe3Q++CdfDP+cduJd/CO/C90HWBtRLPthMBvDfLELlaREHFuZAvOse1oYswy8TZI6DKe8kyO+HIIrg5EA0SSZAkqA8dQaQZcgBa19ZqcS6m26GLTEOVX4vZIEH53RD1KpQGSPi1IMj4C3Lh9zQEDgpZsAnSxJ8996HnSvXQrLZ0CgBrnALGr5Ygs31dhw4cQZyowvFnePgMGuR994L+HSYAp9eEd48OyYfIIdZIJ3Ig31TMRrlOJDbA8nlhWjQwv3G86hc9wmq1s1G3fczAWsDyGuFbAyDNPVV+LYsh2/lQpDVCll0wGUxouq9qdicm40dJ3ahuncaPpl2C0rfeA3aHTsAANqaWvBuJzilCoiIBL98JTirA2h0wqd2g6KjkLFyHS7janFdr5G4LCoNCfsPI2nBNIRtWwPtkT2wjEyDKT0eqv07kfXJBnRY8wsi9xyHuVEN8DyUjY2oGzISAMCpVRDD9Cyy4PWBF9j9wel04K68ghkkajQQ0tPZdo0anEIBKJTgAXRevARqmw2aRjcG3f8sFATwHTtCP/IiSBEWiGFa1M+Zg+oO8SCSocjJBv/SS1D4vcwgbswYQK9nXBAAjswBLMLwwgvMcA4AEhNBY67E6fuewbHbLweio4Fc5lqLDh3gHNqXRdValwHAcxy8fi+LFplMbbglVafdOJzzDTxhGpS98hiOWI/C1jUeDkcN6o80MBl+p7NFXj07G7jhBhYJW7oU+OEHSJNfgEahgVL437NeCIr/wmDoghCKfIQQwvnh78D7aEJZjZW+WvIzrX7/O5o591P67Kne5FWAypPC2kQ9xMC6vszzLWm3AIkaTUvKapM0dU4OyUoFW68HGK+iS5dm0bDNm9a151UMHEh+pYqcF19G3phY8qpULCMlMpIkfVhzdIMyM9kMtina0Sr6seXRp4iMRqrt0JEoIYHquyVSQ1pX8qsU9Msz40hUteIqcFxLeqhS2TzTXbVlR1DOxYyTO4PyN+z6cFr98Vmcj4BwV/1dE4OWkePjqHLjF0HboY2vBc3I8JnD2s70c3LIE2VidZ4lMpazZzpRzrdEGiXRmUNEx1YyzkdsDJ3JOxFUR2TTklW0f8Vy8pjNdCwjlUSlknxGA0lc+wiYqFaQ7dVHSOI4EtUaquvIohcuo4V8CiX7rgL3iAyQrNEQJSQwzQ2eJ0mpJGvfLPIbDHT4+2/JltGTfAY9O9dAe6RQsDIAUXR0Syo1EVFBAdUNH0OSJAVNS3Zv3hA0xdk+IiDodlYZ0WwiSRBYn5sieIGIU21mKkk6LdUunNPSRng4UUQE+bUaalj2bXsxs5wc1v+kJKKePcnZKYEk+a8VHwxFPkIIIYT/KcRHmmCKjYJXk4e+nbbhet4BhSUS+shYyELLvyFelNgbOTAMUAZmvpLEZpM83yJNDQAmM/z9M5lhWlwcM02bPRuoqEDv+x6Aa/BQ+AQliie8gF2zN4H2/gLB74N603ooqqvgMVhAAFBbC87Z2NLhY8fASRJIp23pj8T6NmzWR4DDgca4BKC8HGqHH8bTxZAFLWifGrlDx0BSqSHrwgBBYKZustzCz2jCBRjIKTU8dIkewGAAEhJa9oWFwX7Tneeoi2PXMCgfhg+63ZbVtf1M/+x+Bbg2dlctKj0+IDwc+GQWkNAXSEyELyurpd0mPkhiIjBoECqTusOk1sBrCcfPt10EHgAPrsUkkONAQsAATZKhn/IJGpI7QNBpsfXRZ8FptdD2yYTyjtsBACIvoJn55PGAysoAUWRmdkQwHT0GhcuFjFffgCH3KASnG+jZs+WcRBEoKwNxHItGvPIKiyYEYDi8BzRseIvZHQBZlEDZ2VBOfBg0cCCkRybAf/wX+A7lwHtwKzT7ctHQuy/EAQOQc+t9WLTlEFbsPIbybqkoSemKvEfvQkNmMsrnvI8D675Bfbck5F4+ElX/ug/ct2tw6tslyF+5ClVJ0Vi8cjoOTrgR+lvvQNUn70KePAkNFw2CZ/1P2PnDh5A4wFdXDX/eKUgg8Nzf6JH+XxgMXRBCkY8QQjg//J0iH0REkiTR5z/kksfdSEXvPk6NA1Jp04vXkBwTTaTTkSQI7We/KiWRyUQ+s5nopZeYZkMTCgpIHD2KvIP6EF1yCeNcpKWxmatKRWJyl+aZrHP5ehKV2rbcEqWSJK2W5CC8k7M5KDLPk6xUkqjVsojIwoV09NrriVQqqkvPJNLpSO7enRl4BdbrJZ2uJZLC80Q339w8063LyAxu7JYVZLvFQhRoO5i5Xhuxr1bXRo6MJG/PbkHboYxOQcvUjO7F3rcScvOZw4JmgkiDB5GoUTKdi6bMi2nTmNBWIOOH3nijTbt1vfuTrNeT76efaO22VVQxcjDRiBFBIx+u2EQ68dgrdPKya8mbmUnbbr6bnFeNJU+v3lT57ofs/tBqg35nfqWy5bMgUONt95DMceRTKqmuQxL5BZ78KgX5zEbymgwtGTU8T36Fkjw6PZWn9CKnLoxO3/8MSVod+aNiyNupG4kaLYkGA8kcR1JUBMnhFvJ++CZ5D+WQ7+vPSRIE8gwZSmLHTiSOv4HsdbVkX7GcZKWSvAZTi5hY797N3BIprH3ESVIqyDtoAOMQhYWxY0eOZJGvpgwqQSB68EGqGdmf5Na/jb8IIWO5EEL4/wAkVuKX06dbbeHO+ov2+9oYTJ37+JZEmpZ9LeZU59FOsOM4DjqjDZM+X47Opn5ImzcBDfPmw+XJRnW3blD4ZSQeZ1oGskIBXhTB+/wgnw0Cz4Neew2yWo3G1FScfORR2Dt1wvC9+6ByOkGyzM5NoQAUAuNaOB0QRw+FsGsfBEUdkNIJOHICslYDzuWGV6mEW62GyeuFqA2D4PeDF72ATCBBAPEAJ0ogXgAnSZA5HrzbDXdkNJZlXYTBz76A2jAjPnzueby8aSkUy36GZ/VscFV1UDvtgOQHANhvuwzKwgqo1v+E+l7hiLRZ0RAfBz48AlXTZ+KnHpkwltZgeL+BePWViZi0/XuELX0fxV2KoS6tR9eMRMhaNequvBrd3v4E9M08SBYlUFEFRa+eKLzhTnBVldB/vRjFpmgAHDSVZeiY0QuOuwYislwD4dtv4BOdMNx6F4seOZ2Qx40Bvv4UUpibfT/llbDsPQFXahK0J0oBngel9IDC64dcXg4YDaCpUyBefx1UVit8zipwehX4Ojto+zaQUgD3/AGIRg1O/vQEklZWw/Dhe6ge2wfK/UcRYbNCHWeF3xwB95S30SsrHlHbdkGWCdxZiVuiRgOvx4Vllizckzsbp7t3Qc5FI9Fj8dewXXoFjOvWgnge8PuD3IMAL8vg1GqQ3w/S6aDfvwOIjgbX2Ijw554HTZwITpIBn51FWwL3N/f00+A3b4bi8GHE/bQc9jvvQfzGFeCvvAJ8URGzvL/7bkBrhqTRQDCHA1otVPO+Ax59AbADfrMF9sXfIYqTgORkbPngLQin8zDGbEZ911TEfvcN45ekpTVzS/zdu0O9ZQvw5pss4rR4MSg8HEUzpqC7Xwd51DA0vPogLNt2gSNA1ijBu30sGjdrFiIAcBERjBMSERH0mvyvITT4CCGEvyky43wQKRoKpbl5tNDyP7z1f/PAe2q/jZqPC5a2e57HB035Pev4wDFJFi3qo1wYl9gfjX4PTqSnQ+jTF/46O9RiS7ibF0XmbCqKgFqNnOkLMPzxuyEOGgRlr17ImjUTfHk5S6vU6iFEWoDyCkCrBdxuQJYhlNcCpRsAhQKq6//JUnUB8C72sOV9PtgH94V5Yw4Ejxu8JLKHEAAZgOCX2OfAckuTIJq2thpXblkFc3UFOK8Xr/zrLgheL6DRQHP/s3Bffg3EpFgoThUBggDjpsNsuURrQNRJFxAdg9wVs3FdBQ/zkCFIXpvIHiI8jwU//wSuZ0/gzBnEjboDUv+xUGg2gHRa8FwRuOoacMtWgj9wBHTDDeBydiKxqhIqQQvOYQf3zDOQzBYoywpg2bYVcnUdtAoZnN0BbsQocE4na8vGQShYBoqKBB5/AnC5Idz7DAABOqUF6KwEVCpwU6aAbrwRfEQES+tMTobw1NNATAzsUycj+saHAbBBGjgeECUobW6kTFwE5YQnANcCxCUMBOxGIDoG9q8/RFx4JlTJyTA9uwTivB8hdrGAz6uHwuEEabXwkgiN14NV116B7rtXoGLE1RCilOiYlY6of/+MUws+hkMjI2LDOvCi2O7OY+m4EkiSIOr1UDQ2sodyTQ0EjgMmToSsECDI1LK8BzCi6caNgM0GqFQAABVk+APfP+6+G1i1ii2lDRwIT34+9KWlwOOPA/PmtbQvCLBW1CBKxwMREbhKa4H/5TdBnhdw6J+PIrZpOWr5clagsJCJiAHAtdey65yYCBo8CI6aMoCPAScSIvIbAYn1VXC3crZWKoAwA9DQwJbQWqfk/i/jvxCJuSCEll1CCOH84PPZyGrd91d344JRZC2iTw98QrOO/UzLNsylb598hESFkuo7xLcJlTcTAgGydUthIebu3UnKziZHl64kKZTkMRrJr9GypQ2FghH51GoihYKkzC5EMeFEfFujupa0Tp78PE+usDBq1BnIr24J4XsFlsrZaDSRN5AK6uyRQnTddSzcnZbW7L+x77032ZLEZZdR1bV3kiuuKzWkZJFs0ZM87homp52YyJaG1GoilYrqM1OY4RvQ4m6r1ZIvjLnbtnFjDQhoyQAj3Z5liHdo8/dBxb6k+HhasbmgrTiYxUKk0VDZs2+wdlWqNu6u4uBejNRqNLJjZ85k5N8m75qkpGaRsao1c1mKaSviZ9P1lfSB72Ts2DaE01N7plHtorkkxkST/fYbqKF3F2pM7dxmycRj1JCkUNCy60fTrHmvUOGWn2jp7kJa+NFkkvRqkgSOfColSRxH3kBKthQQqmsSIfMH2pdUTEqfwsKIOI5sPVIDhFSBXBmZ5LdYSFKpyB1mZNe5ScpcrWZOs3o97Xn6xWaTOdJo2oiZkUbTbllLioggR2p68BTnlJbtkiiSaLORb+dOEnU6EjMzSdZqqe71d6hozlISwwyMOMtx5FcrSFIrWZr32ctMsZHse+T5C/J/+TPwXyOcvvXWW+A4Do899ljztgkTJqBr167QarWIiorCtddeixMnTvxnI6QQQgihHZRKI0TR8Vd344LRwdQBbicHscGAfQXdEGvojsKew7Bt3EOMQCgIqO/NRKxkiUUf1MUFTHK6uBi45BJ81X807Ho9ajJS4U2IZxWnp8PRMxXk9YIkCdyRAuCB6wG1GlC1Tz+UOOCnG66FWxuGmo6p8PYfDIclEn6NFlJyMiq6dAWnUGDHLfcCALiyUtDy5ZA4HvLJk7DX1qMmNQP+khpGdN25E1HHdkLJ+2DqFA2xbzLkRQuYnDYRm5VedFEL8fT995mY2KhRQO/eLbNwIjYDjo1lx8XHA34/XA9cDuTlARMnArffzsi4vyL2JfUfCIXAtey74w7A4QBkGbHvvwaYzWyZ6uyZst3O0jptNuDhh1m/3O62omFeL8xPTWmR/G4d/eJ51K+aBiQlAXv3ttRfXo4uo19ExO33Qnj4ERwbnA7jwXxoj7cIwMk8B5XdA5JlDNuwH/ff8yp0t92DUTddgug9P8Ado0R9Zjy++uxD+NRqKP1+gOeZOBwC4l+SzIilADifF76hQyFzHEilwpdjr8X2QReBkyWIsgSZ4yALAhRuJ4gIJ0Zcik3/fgz2iEiseO417HvgMYRvWwVPShIqX7wdjutHg2QJfnsNwAGyKMJrMkG8+WaUPv88iufNA2w2WD0yGg3hKH7zIyz5+QxWbS1Ceac06IoKIBoMkG68EfbHHod12Q9wr1sH3uNhqeXh4ZAWLkX0gB7ghw4BN24cjj76MrwR4RAFBThBwdKN0WpRs7KWRfWuvjq4nPv/Kn7vCGfPnj3UqVMnyszMpEcffbR5+5w5cyg7O5sKCgpo3759NHbsWEpKSiJRFM+r3lDkI4QQzh+1tVv+6i78Luxdv4i2vZNNsx54gbY8PI7qBw2m/AlPnUUaDJi8DRxIW558iahTJ6K+fUn6+ms6etVo8iuVVJqVQT6lgvxqNXkNYS0iYQGDOlIqW9Jwz5oxSiol7b7udnKpdORNyyTpkovp+CXXM7GqW+8MTj5VKkkWBHIOGdo2QgOw2bIgMMJrTg6bpRoNLcTLJjv28eNp+eqvGHmW51scfJvaSEtr4/xKCQlERGT7+H523MCBLTbykZFkS0sOLgEeEUl16X3b1hWIJDX3ecqU5uMpMpJcyUkkqpXkTE0i60W9qOyxceQ3msg17vZm0TDr7G/JOeAiOv3uiyQpFUGvk0+rpgNvPk1+rYZW719N2UtmkSsmkhbunUaO568nt8VIqz95hxrCTfTTEzeQyIFEpUBfPzuQkU0tZpr74JXk5xlJtDoqgRotaqruEkaiwFFlp0SSOAQlqra5hwIpuU3iZH6VIkAsbYrQ6Ek2GhlBVRCIwsKo7P33yR8WxoznRo4kSalgkaeRI1kqN0CUlUWSWsnur9radoZ02zfsC5rK7Og/hF3vs8zlpIgIch8/Tv68AvIr1CTu3sPulTFjyGMwkczz7By0mvb3JMBEzQYN+i/9es+NP93V1uFwUPfu3Wn9+vU0cuTINoOPs3Ho0CECQHl5eedVd2jwEUII54/a2uy/ugu/C3aXnTbtX0Uvvf8lzX/wX+TS66g+No4tefTOIldyKvlMZvLGxJAoCFQbH9PyYM7MbGb7+9Xq5geQFCwk/RuvMnM0nRh4KbkTOpF70wZa/frrVN+tExWMu5rc4WbyqVTNoe6G2KjmDByfXksSz9PxOXPIGWEmmeOo6qspJEaaSbQYqXbak+Q16+nAsukkatUkKQRydutIfrWqWf/DEx3BHohhOtYGz5Mk8CRGWMh+1w3kuOcGKpz+JomC0JIJpFCQOGY0+Sc/QeIlI0hWKsiV0pUaE+Ko9JKR9OPeDfTj3o2U8+mHJCoVVJjSixyxSbR/SG9aO20eecKY4259RDRb4oiJobx5c6lw2jvUmJFO7jAtUdeuLIw/dGizFw7p9exhGbB8t/bsT6I+jOSwMPbw0+uJLr64WT9DUqnYd9S07LJkCRHHkaQQWGYQxxG9+irJMTHk0qrafi+BOsrDw0niOJI4jqwxESRzILcqMJjgOPILChKDDH6Ia1FCLZ74GPlj40g0GMmr1dE3DzxAXoWCqrpGkDXWzO6lUaPIa9CT+7priTIzyZ+cTPbOndmSEhGVXzqARJ6jh39ZTTR7NjvfZ54hd+8Udo6TJxNNmtSyTNY0+GjtbdN68FFQwLKwVKrmDCEpKorsG5huiBgTR1VjbmKDD4uFPCYLSQolSUKQc21a8goLY3WeOvVX/qz//GWXBx98EFdddRUuueSSXz3O6XTiyy+/ROfOnZGUlBT0GK/XC7vd3uYVQgghnC/+t/1dzgWD1oAT1XXI13VG/+vvgr13b/jvvwkAoKqqgKa8FILTCVWXLhBkGWFKDuA4uL1OnHrsNsg8Y/wfuf825kwLgNfqWAYEAFL89r82r8BBK3mhqTyJT9/8DvurdYjomIL8NYvQ6YlnoVGqoRw0CFx4BMBxMNQxVVYhMQlKpxsAIeW556BudIMjQvSLn0Kot0MwhSPitU+haPQi88U5EFLSwKvU0M34BAqlClxCAiqevwuqwUPB8TwU8YngMnuBUyrBaTTgY+OBnFw4NhxGnq87+EsuBX/ddTj54IvgOnaEUGuH4vX3IJRWg4uIhOOrudDv2oOEHXsxtnNvjO03GsNS+wKRkSj54TN8+e716PnLEUR7CqBSCuDUavz81Q/gEhKgrqpC1yHD0PGGG6GyhKMqOgKelcvZUtXhwyyUz/NseaZ7d8DrhbhgAZyiE1ysBz6TDFmjAbnd8JWUQFawHIaa2+9g2h67d7csuxAx8q7bDeIA37R3INXWQNOaPAlgb4oBMgfE1teDI2LOt1V1kDgBKp8fBKAysRP8ggCIEnx6Pdw6LfxK1rYstOiGJH78IeoHDgCJfrjSUtGBB6DRIFwTC390DHt8+/3geAHHPp8JjB0LobERO996C0hOBgC4hvaFqNeiC04xQrNSCbz9Nhpvu4plWH31FfOUmTu32cMm/dF72i5TdeoEzJ4N7YkjwDXXsGWslBS2XDJrFjhZhmbiRGDQIAgL5qHu9gloePIlYOBAFPdMxt5/3wVfmAGSMkiOCBFbQuvcmS2T/U1wwYOPxYsXY//+/Zg6deo5j/n4448RFhaGsLAw/PTTT1i/fj1UAfbw2Zg6dSpMJlPz61yDlBBCCCEYOFAgC+PvhpRYE/7RPwEqjRrRMbEQSAQ4DlK9FXA64dNoUGWIgKjVwdqjK7grroC2ogY9wrqA1+rB6zTIvOwGlqXAATAYwMkyWw8Xf/uaqCWC2WFDQnkphuybirgOm0ERh1FRl4vqhiLIdTWQ9u4E1dUBROD9IuOdnDkDDB/OpNiJwHu97AFdVAQ88wzjOkQyO/MGpxV04ADyrxyFopnT4JMkVMTG4FvRBd+2lWz+GmdhAmkcB5njIJIAna0B+f2Gosu2ZeCsDcDSpeg6933WdkkJO4Err2RcE9A5Bct4AAMKjPBqdeBzdgHde7TsvOsuZngXeGDxahW8WoI1KRJ45BF2roMHo2FAL8btOHYMiIrCdnUkPuk7HkVCFOwKDU5ExMKv1WBDl26QdTrIShXqwmOAf/6TZWDU1bHzi4yENyoSeOstiFoNeJ8fdRmp8BkN8Oh1KO03EMRx6FnogVenhk+pbJMa7uwUCUkQGL9Dp4La5wVPBJ7jIHdOgEBNBnQy5DA9uORkcCYTonfsgAKA+fhxDP7sC6gEAQqHAxGHT8Jur4P4y15wsoxOV1wLfPghuLlz2Zg+Oxvo3x9dXpkNldODR0dOAv59H6AUAIUCEc++B1mtQvnE21H5xL9QP382SlYuANms8Pi88BjNqJkyDds37se+b1bBn9kLkizB21ABWauGPT0W1f8cCbmsFLBb4fXa4Nfp4Jg8GSqpCMVr16O4rARJR48hauN6qG0NkIPNNXQ6ICsL+Mc/gLVrz+OX9z+CCwmpFBcXU3R0NB06dKh5W7BlF6vVSqdOnaLs7GwaO3YsZWVlkdvtDlqnx+Mhm83W/CopKQktu4QQwnnC4ThBbnf5X92N34WcIz/TzmMn6ejmnUTjx1P19BdIjAhn2RxjxjBOxJIlRDxPruTOAbM3nmSFwJj/YGZj/oBEOplMLaJgF/DyCRz51Txt+uxtmjXnTXIbTVTZLZ68Og35AhwBUcGRs1caC3MPHUqyVkuiWkU0ejS5OyQxPkrnziwLJSOD6OKLqWLkAPL8uJwZmcXFkahRU3XHLuTSGcjaIYmdS6yFSJJYKD4sjKTItnbsW2YvZBwPjqP69L6MsyEI7AI2276nn4PzEU62rF4k6/XkXraM/Q9uMpA7hzCZx2wgacjgFk7JmDFMkrx3bxbeX7eOyjf9QhWTHiWvVk3+/n3Jo1HTp6++TJfPnkv+LVtIUqmopu8AVsdDD7VkuygU7HviefY3KYlcffq1+z6altG8CmX7fQEjuV9e70Pl/WJIUggkqVVMBA4gWSGwF9gyljsiqqWsVkc+DRN+c3buRpJKTZJCQe6YWJIUAvl0GnJ3TiRfhJlKH7uD6npl0P45i6g6IZacHaNp/eKJ5Is2EyXFE40dQ/Tg1YwD0q8fu379+jVLz5/esiQo58MzegRJYbqW5RKlkmVCJSTQ8TkvUP6Mj0jWaolqa+nA3GkkCjxJEeFU0iWJXAY9eVVqEgWBxMD5EcAysP5HcCHLLhzR+ftyL1++HOPGjYPQJH8LQJIkZn/M8/B6vW32AYDP54PFYsHnn3+OW2+99TfbsNvtMJlMsNlsMJ4tRxxCCCG0gSR5YLcfhMUy6K/uygWjsr4IS9ZvgJGPRqrsQtbd/4SiKQTPcSCFEvY7HoLp6xlwJHSAoegMiOPQmBiPsPIquJLTcTxzJPp+N5NFITgA0n8WBRKVCsgCh+yxV+LipavAyczGXVYqIPE8lF4fGlNToC6phOBzAX4/JLUKSo8XJ194FonfLYW1dy/ErVgJUeDBSxIauiRAwykg3vlPWArL0XD4BFYPMyNLHwbjmUoUPXgN1OV16Dx9BY5dkYJuJVFQFxej9tZbsaXYgzRnNQZPfRHffPYVrl//DcIWrULl/h8hlFbA8MFcnJxwJSyHS6DJL0HVhJtARFBW1CJyxkLMvHcSrjh+BObCMzh6zwPQV1Ui87MZWDTmKjw6ZTLEMD38YTpoq2rh6pgIVWEpVr34IsxeF+IOHka3TZvB+USoSIZr5Ejwbg/U+34B5/GACHDHd0D18MsAjkfevU/DlLsXfZ/6B6rSO8NU14iGzBTsm/kaLHsPYeC9z0Fld4DUSnBeP/zREajXc4guqIWkDYPgbgQHwG9SQ2nz/ur3RDwHjgiyxQje7oKsVoAnHujRA1xVFaiiAlzXrkBREWQZ4MN0wNGjEPsNhFBVDufH8xC2ZB6QkwOsWAG6+mp22xkMgNcLRESAamrA9e0L/+HDULrdkDPSQCdPQ1AogYsvBv7RG7j7fcDjAYYNAwoKgC5dgOxsuDNToRU5Jq3/xBPALbcADzwAWZbgKcuH7lg+i5AVF7MMqFOnUPX6/Yh+/F2Q0wnZYEDd9ddDt2snGlUSKq57Hn0WTgVOnmy+BhzAloGaom7/A+JiF/T8vpBRjd1up9zc3Davfv360R133EG5ublBy3g8HtJqtfTll1+eVxshwmkIIVwY/q4ZL7Is0YGDC8nn85GYl0fyiBHks4STP2sIyeCaiYeyIJBfrT539knTzNYUXGr7fF4SmLS2NTyKSWsnxJPdqCefUghkTbRIv3sG9qeqx2ex/nEgT5iOvEYjOTp1pJKnnyJ65hlqTE0lSaEgsVMnpgUxcSKLAKSkEKlUdLJ7OMlNMttNhm8qFVVlpbbIbzdpNsyc2SzxTgAzHGtVpjGrR0uZ/fuZJsXAgSQZDCTqdCSPHEmyTkf+hQvJc+IkiePG0Y8Lp5J/3LUk9s2ixhVLSTKbqeHEASocPIB8WX1YuxkZREOGkKRQEqWmskhUp06MWDpmDHnT+7AMEYOhjSw4JSRQ+aHVVLNrA4kqJa156wraP+8Nsqd0IlEpUMmWmeSPtpAs8FR2x2AWWRo8lHw6ZuyX8/j43/y+3FGRdPK+PiRp1CTr9VSb2p1qemc1RzxkBLKPAvcHcRxRv37N2UliRCQjaSqVRKoWwmuzZDlAktFAVFBAXm1As0SppGMPX8GIogBRk5YJxzHZ8yY9F5WKXOnJ7Jp0786uZZOsfZM+SKt2mqIg/vgotm1dwBAxM5Npr4y7jn586HZyRES2JZqaTEyPxWQievHF//4POAj+NMKpwWBAenp6m5der0dERATS09ORn5+PqVOnYt++fSguLsaOHTtw4403QqvV4sorr/z9w6kQQgjhVxBM5vx/HxzHw2trhK2sDoUrNoG27wDvFyEcPwiAmGKmUglIElMQDYZW8tonrr/ynHqtvxXe5QA49UYY62ugcjkRU1YOvd0JIcAdEWQCBbRC1Bodon/+GDCZwBmMOLlmFlRHjyKsugb5GemoumYs/BwHd1YWpNWrGXlzxw5GPBwyBPD5YNFHgfP5GFejiacRFYXNs19mCpdeb1v+Bs/jwN7vgDvvBBob25QpnP1MS5nFi4GRI4HFi+Hv2RMQBHBbtoB74QUonn4aarUKAs9DwSug4BUQ7vkX9MtXgQdgVpuhJoLbG1ANvfFGoKICPqOJ8T2io9ksP8A54WSZzbx79GhnRHd89z74qvMha80IXweUVXIwmGLAgUfiyAehuG8iOAIiAxJQPAiCn0W94rZbQTyLoHt1wWfPqpo6dP/sADiPl/GDrE4cmnA/RKUSsl7PjOJaq58SAfv3o0mPhK+rZdfR729Wvm0+LqBoynk8QGkpFF4vI9wmJiJmTwEjj8bEMKXTpjLl5Sy6UVsLREWhdObrwMsvM10VAJIoob5jd8h5ZwJ+fwQigpyVCVIIIIGHUFEDSaWC/5ZbUTZlGjxWO8Rt28D/+COumrkQfp+fSf3HxDAyrNPJzvGFFxjv42+GP9QCT6PRICcnB1deeSW6deuGm2++GQaDATt27EB0dPQf2VQIIYTQjL9nxgsARMgGeJxudOzdHTwH8I12cG4X2ylLbb07lC1CYU3DLUmlgiiwDICIKDVEvRkAIPJt/7Wdz/AszGEFABDHI+fe59jST6AesVMHcH6R1eN2A/n5LERvMLDCAYfYfqdOo9Fma3nwJSUxP5CaGvb57ruBhARs/ehZ4L332AOktetro4R5336PChWH7MsG4MuDu7HV7YeoVKJcUgBPPcXqHj+e1VdTg04PvAOMHg2UljKS5KZNwIkTcE74N5rvjSbRsk6dgO+/Z7L32dnAwoVswEIE3HEHwg8dweQBt+Gid7cwAmNyMqw90lgdTWJpNTXAhg1QHD/MCK9TprTUH8j2GPThEsTf+jisk19F5tJvMeaJp4F33wVkgie5J+R334Pt4ivhlpSAWgM5eyvKRtwGAOhw+hA4mQ0A1K7g2Y9c4Lwar7kKnEaD2MoKjH78ESiJICQmggs83CkgUkdgD3zIbDApq9QQzWa2vRWptelYWaUAfH54b7oRnCzDp1ahQSPAsvsEaof2guhxQXa6ICmVrExBPmjSJEg6NaiqEgn/eAD07LOQG+rgSumEskXvIsx1DK6uRnh6RgACz4irVWUgo571SaMCp9VB8HgQ/8kM+CZNRnGXZIhjr8HOSW/C2j2DXZeqKrb8IorA8OHsnP4ukuqt8B97u2zZsqX5fXx8PNasWfOfVhlCCCH8fwK9JQxxPTsDDWUsXdBqAxQCm3UG0Dy0OstEjAMg+HxAYPBhnrsKSlcjJF0YRE4AfG6AAEH0gwOBOL7Zn6V13a0HJqJaBWV4BEba84GwMPbw8vpQ260nohqsUFhtwLhx7OH/7rsAEbo+OAXILweWLYPussvQFcDpnK3oMms2uMsuA3JzgWXLWhqpqcGwB98AzlSwh3UTsrNx0+ljiD1TjHnPPgHyiFjgqcGWjgmAz4fBEycDx/PZA+fhh4HJk1m5RjdwuoSt/bvd7DrdeCPMosjs6YcNAw4ebNMHp+xkEZLvvwemTwc+/hjYtg0n33sLt2w7gQlX3IN8vwDhqSmIuW4UfJmZUOTlwfrI49Bs2ARZqUF9bGckrvwK4u3/gNDoQPFr78K/6yhi0nph47XXIq2yCPrdG3EqjZ2juqoK0WlpKPngS0RuWgt9/mnUDL8SvQ7vgLf3QMScPgJRo8NXoy/D3UsX/ua9Q+Bwpr4GmX4/OADusDBo6+pg8/thAgBZBgXUTwGw69Pk0+PzAr5ANC0I7ZH3scGjuqISBEDldEElcoAgIHLDLsDmCPj+sOwqiKxewe4EeAFKQQ2uRw94tFr4EjogbsHPkA2doD90BLJWxThKXj+4sjrWIMeBl2RAz4Ezm0AOHsfnf4OOsgTlmjXocLoIsccPs2NVKpYFFB3NImp/pwyXVggZy4UQwt8cHKeALPvB8+0lxP/XcVL0o+iXUuDHbAxsaADJBIhtBxkyWIiWOK5Z06P144KXRIDj4eyeBmvfZET/vABKXRgESQQRwCkUaBzaC+odB6GUAGuEGZaaegCApFZB4fWBBB5+UxhU9XZQRQWwZAlkpYoRTiUJsT+vbmn3tZcBWYZs1oOvc8BniYAQZoLztbdxxmsEyYSwUg/gcsFubYBGr0fjqy8jr7EB4Tt2orvPB8vpEpDXjwY1Yd+6T2DOPYO+Viu84SrYTUYM2bwV7/XoCwO3B994onGLQgGnxw2DOQxupYTyKQ9Be2A7EkxhOPr8WGS+vBSqyjqcvKsvekzKgz8hEeX9BiN841o89MCTuPWXHHSe/hHqiT0k04qPwVafh9JdHyFPLsPl5WVY+NMKuBP74q7jC1HXaAMiIqAQOHhTekGcPxvK1avgXpMNkzkMHM9Bu/kHcCAonQ7gvnvRqb4EGJwJ7MrBRfY6mBUKwGZD4sAhjAy5bRvkE8fR4fUnmuXbEy8dDpIlqMN18BqM0NbX4B/rlgTSjglOczgM9fXNA4im750LfOq1bQ8A4JfLx6H/v+8Axo+H/NhTwEP3s+MCkY5ms7nfuB+bZNqb0Ob406eBLl1A+fltyjQv9fE8QARrz14ov7If0j9eDFWHRDT06QNjWRGU/7oPeOgh8I0BF2GlEoiPh6hSwaPioTt6EiTJEFw2cAkJGNhYh/KAronb5YXAgQ06oqKAadNYOrQo/q2cbNvgT+afXDBChNMQQrgwOJ2F5HTm/9Xd+F344fQqIiLKnj6D/Ab9b5JCm96LqhYioT/SQqRSkb9nOp16/WOSlUqi/fvJnZrJjrVYyGcwkd8UzsiK0bHNdTkiY5rr9AfSd/1hBnIkdSYyGJrTN8lgaCEXxsayV//+REYjFa5b3zatkogoJ4e8EVHktDW23bdkCZFGQ0e/mkc0YwZLnW0ijyYk0KqfZgStixISaOn2Ne3rAkjUaZr75uuUyN6vW0fVP+8ntzqM3nr7C1q97ADZuyc311mxaBrR+PE07/vldGTZQpZKO3UqnT5aTQ2vT2sxqCsooMZho0n0+8nr91Npx85U3HcwSxu96BJyJ6cRXXopU/dslWqanZ0d1OzOExFOBbtWUeGyz8gXE0n5wy4lX1Q0ERF9/di7JANUfWsCM3/jWr7z1vLzzYRNjqPG8EgSeYE8ShX5tOz+EXnhV++jX3tdaJp26761folNMvaxMSQrFGR/82USNYwQLSUlNpOp/WF68iYlkNytG0k8T7UXXUb09tvs/po1i2x3/IMawiPo2mWbaNvQy9oQVAlghNam6/s/gP+asVwIIYTw10OjSYDbU/ZXd+N3gXNL2HGiBnVhkXAkRAAc4G/F7aBW3A0OgKxQsr9qZrCFsDBIah4kieCqykAdXXB3iUf19Jfgl12A2YzTC5ZC6JgELwikVEGzeydknRYAoG2oAZeRAU4QoNi5G+7oaPBeD+Y8c32zyJUrrSvcd1/LZrhGIzBrFjNfczhaiH4BzsfZIl+yJJ9zH0aNYjPZjz5q3qTmlOcUDGtup/U+jQYF385kxE8AitIKRkbcvBmS2gtndBQu2roJ1fxeeMfdDO+H76Hu9F7Y9fmoe3kEYHciXzsA+xesw/TOV2H77jL4b7mnDYeA4znctnIj7lu9ARuvuAHCJZcCs2dDEHh44hKZQV5sLOOzbN36q2Z3YkYyIjtnIKFjbygSOiDplWcAiS1x1EIFr0qJbZU+iCRBDgiK+RUKtmSCACeDC/A0iODX6cHLElR+HxRuJyR9GIRZM4Pea+cL7hzv/197Zx1exbX14Xfm+DmRE3cSJIQgwa1QKNACFWrU3YXqbXurtwp1d2+hLlCDUooUCsXdnQSJux6f9f0xIUHSW+jXwm077/Och5w9M3vvmT2HWbP2b6/1a0gLHzUY1P+uqCDkjsb2ybcojaJpZfeepqkeU109lt35yLZtKJqG++dZyF13Eaqsou75V6jRwoisKOfLi0ZwzIIZ+v2SlKQvrXW7obDwL6n3AGPaxcDgL4+qmnW/wF+QzN2rUGorCN88BcUXImQyY7LozyOTQG56GPFlfsJqvQRcLhSrFaWmhtrBOYStzcNaVockRqKU1mIadTrtjx0N5U/gXFeEbNmOJ7sTpcU7cbVvR3J+PqHwCFSgITWVsK3b8A0fgnPWPF20d+KJYDOBFuLctydiqa5GTCacmV1QehyP8JEusDz3XD2U9UknwXPPkXbGafoqkEce2e9BqwYD2EeeAOvX4X32feS4YThKN4CmkTH2UcgvgGuv1Q2YRqFmr9uehp236/qM4cOb6sLnY/D1d8D2nc3bfvkFYmLQRPQIl4sWUXrL2cSZ01FmzqRm+LF8dk0WoaFtSdwym51ZbSBgJXnWUijtR53qoFOslYCmsmpFEWef2BZbhQ9F9nnkZmRQ/cSLvJ4RjynCxToiST6tP+TlYVq1AlNCmm5s7O1TXh707MnAb77RH7Bt2+qahJEjAbCv3oCcciamDZvg849R05LQ/AG0YwYwZvkKAmaVzB0NqAKlD9yJ5Y13UT0hLDVVACiNRsfeqL7hBfrKm5K2rYnfnoupvo7gTTexf7SpQ2ev8bDvNM/eab9DZd8Mu4o/gFpVAWUlB0357GfkKApiNrP7pXfJuON6TAMGENajB2FffIHY7Wx6/yuSZ0wi5r339Smrqio923Fe3l922uWwgowdCYwgYwYGh8+G5e+iaE60kIYWCv3qApiWQrE3/2d74Mw6CMLeTBki+0W7PoADZ8sPLNv3wOaynWtmYPPGUucPoO5ZyKmvrcTnsGH1+lFF8Nlt+NPaEL51I2K3oHgDBI/vC4mxFB5nJ+GzrWy9ujUpy6Ix5+XTcOJAbO9+yYK3ryfu6Qn0+GQFnsx07Nv2sPq6m+nyzhv4k9NxbluPx27G26oV0Zu2EXI6aEhPI3zjFjxZ6ay+/VLazSkg5ssJ+COjsZUV6112WMEfJDA0B/+5x+K85wMWPvEwEcsraf/hC1jrapouVMhsZs2NNxAmQcLW7WTXuedhC5bTasJHTD6nLz2LGwjLzWf7FWdgLyoj851JTLnsBIbsseHYtp2C66/VxY6FhaS+8hpv3PYQQ9auI3LrJtbeeBfhP77DiAfGUZlsJzrfgyclhq29O9JhbTE1fXpT4/Oy9lgH4eF1zG+IIzOqI1LWwLGRx6NuycUXv4OVNdGUxMdz2Z2XEUzLQGqrWHbuTWgjz0DbVUv/VlFsLtvICudaYiNiaahVCYuKw1lYTNdnXsW1YR1adAzW2nqW3n89NenJ9HzqXSgtJ35PKZrDgamhgaLLLyfhww8RRQjFO7Dk1yARTtTyGoIWC4GkVMx7dqGoKkogiGYyYQkG9tf5KCoSF4dSWqLfTS4XAQliqffijXJhr6zX9zObUIKhFu/IQ0VMJj0PzX+52/feyS3d5YfTbjDMgbnOg+Z0oIiG4guA1ULwkrMx/TAbPB6WPfQk7ZwKUVdcpbehqrrw9KqrICXlf8b7cTjPb8PzYWDwN8AcyCI9pzuq2YzJZNbfEH+NFrbp7yCCoqiNuxyZ2CEV1VE4433U5+eRFmbHF76FothEQumtabthDdQHCY91ww4TWlQ05kAIS3EDzJhCq19+gbnDyM5vwORyQ3U14fc8QZlpMvb5BXSbtA41qOHzNFB90Rn09NbAwGOwLl6M32bDpQm7BlyCs/AlzIqG+fFn4fRROHeV0Pvlr6k9+STUsDDs03+ACy8klJeLactWGDwY69oirOd1g7JXyH71baJ9gLcBoqL0+BEZGZi2bKHba6+jdukC1dUknzECNm6EFXdwsreaOHM4VFfTutdovXzJw5xTrxFu0oWaSS+82CTUZMFCLi+/EVUCWGvrmXV+bzIaVxoqJgVVE1weSFyznQZ3B2K++BxHciy2JV7ePaUTSWmt2GkJopWXs8v/PTmmbrTxLqPa0oc0j5X85DZsvu8icorWMfC997A9dTszZm0nYmgrOq6rptugc7HkdNPfuseOhZ7HgftzdvTJps2MFdC1K4PG/6DHG7FORMtdgxoVjamuDhISSJ4/H449lpAZGkYPJuq2pyApDdqHo6xeifOTj/AffzwWs5myNolE7SlA0yxoIlSFRxFbUYInLhmLy4alpLjxvE2Yq+sAmgwPAKVx5cnhGh76VA74nS5sDXX/fd/DrPvXUABLowDVpKjg8epTey4Xlrc+QjLSoayC3jdf27RMGKsV/vUv/Z4JC/ufMTwOF0PzYWDwN8Bid2BzurBYbagmk/4G+WsfRTnoo6oqqmpq+n6kMCtQU7ODHj0G0HPUTQS7ZGFzxMLdb9PgiqNg6MUE23WEhATKP3wdVq7Uk6uVNy5RjIvD8+DN+wXZCmlBhh5/JfXdMwl16kD5MV1xzF2C96efqa+qp67PQN57fyrYbGQv+BQrAcw11djOPRutfXuw2zF//DERX31Ng9tN+ZNPwtatqB4vHH+8HlzKaoXGsAKqpsGOHXqCO69X1zlcconeP1U9OGhYXBw/v3hXi+VbXnmp5SBjcXEUvPEGCT/MIkpMXPrDMoKOEKEwC6/eeizbe2Tir61i7Hlt0XZtY2daNktHnMC2i8dw1pQCvrGdTJnLRbb8QvWeNZS0msBs70BS95gZUDyNCLfQrnYrafdNwLIjF3r3JmbJzwA4XWFUdusMc+bofbv//oMHctSo5lgmQGMkLX1pa2WlHqRs8WLUXxZhmTRFL9u0CebPx1TXAMOGYWloQCIjUcqqCITZUD0ezF4v0dVlEAxiL9qDeddOQo1Lq6mra/I0NESF/7/uw4J+XUEBLdJJyA5B8+G9l7fk8VDgIM2IcsC2Jp2I2Uzw7BORVm6IDoP/XAp2K0r3NLyto/Vlww47itWqBzybNUsPbDZtWvNv4S+GYXwYGPwNsFhtBPaJjfFXQQGGDLmNkuJ1bF71PeKwk7x+Ee1GZOC6YwxtjknEcs1lzQbRvoLLxsBce8sbwmzMGNqa9y7LAUBFJZCRQpubHsUW8lHRvwvmgp0ApFvMunCvogJT//7UZGRSf8yxeoCzcePg3XepC1axM9rH1Mg2aIBmt+tGBsBdd+n9SE5m5fgn4KGHdI+SxwP5+U0xOEKJSfsLLw/o88HlSotCTfr1oy5Q17Qt4vmXGfDvJ7CGxXJa0qWkhGJRI+J4ZG4M4WaVqGgL0cf349PYcMzio97+H3oUF7EzfRCjLemkVwVICssgLqwtW4ojiF27kTaPTwKfUPji8/D117Qd/4I+Rvsao1VVulHRmCI+aflWPY7I3pTyGRkwbhzBMKd+PVJSdGFu9+56tNXMTFxT5ut17Tut4Wlcfrp7N9FlJdjKqpvWjqg+PQKpKhpqKISpUaDaFIkUcFbWNv19qOxrKJSoDkKKilLTgKOiHnNjkDhPUtQh1dPS8twDyw/cBiAmBbFbIBhEmbsQyuphVxHc9jx0y0G2VFAU8CAqaAN76YaH16tfm8svh1atWhYm/xX4s5feHC7GUlsDg8OnrrJCinO3H+1uHDbzpnwrIiLL5r4ly+a/JRuWTz54p73ZWzt12D8Ta2N5MKuNiMslS8bdKCIim7YtFsnNlUB0pHj6dtvvmNzPHhSJjZWKbr1Fc7lk29PPiIweLSIic178Qc9V0kfPyFr84bOy+Ns3ZeXDT4ukpMjahbObl0GKNOVWqeyWrbeRkiISHS2a1SpaXFzjMt9o0Vwu8U+eLJ6GBvFu2CBaTIwUdMoQzeWSusnfSo23QRq2bBItNlZqO3URzeWS4NSpEgoE9c/WbaLFxkp1l06iOR2ihYWJZjaL5nLpmVtTUkUDaUhJl6DdIcH33pdQVJTUprWXgM0ir465S96+92S548Vn5M2XZsrsq8fJ1v9cIyUvviRFH30gdz79pGzokClTnvu31KVGy4YzL5W1E2dLUUa2XDNppVz23mSpDXNKZfeOEnTYZeM7T8uKrQtl87zJsvbYjrIif4dsf/l5KbjhWlm6e6usn/SpBFVVtMhw0Rx2CbkjJBATLSGzWerTWzcvl7Y351QJ7c1Ke8AnpCjidYdLUFElpKpS0XtI83ZV3W/59e/9hBRFNp11gawffLrURifIzlbx4rVY99sneJhLbw+nfX/vDrqZlZwsmqro91JkpJ5DJi2t8foo+rLsvSbZvtmI/4c4nOe3ofkwMPgb4Ix0U5K342h347BZ+HMFOyyf4I5IIKJ0C9kX/YuSjE6YvR7WXXIrxb0HE7tuKYOqqqmODMMd7sA37j7Kgruwrd9KYlUl3jAzDpedrO9/Zlmnh+l6+TPUpsRg83vZcHJ3ctZtJnjZRfhj3QSHDUCrrqEqph4Jj0A+mMCuhCTmrd1E71ARIkJw5w4Id+B881Pi7r4WpaYIyc+n/dARiC+ALzaCFYufxjRrCX0CAUpqvITsYQT9QnR9A77wKH78zwucecv5KNVV+OLiqHvmGbY7HYSvW0uHmmpqY+MIc0dQ+uJz/JzkIHbFak6qrqIiMgJbuBv/489RpsYCYF27ksSqaqoiYzA5nTjLynWvQr2uc7AUFoCioHiqwWFDvfpqxBGG2deAosGlH7yJovrZeVYe6R9vpT45Dpu/gbDqD6hq4+Tf5QEUVzRKgxnnngo65H8IX39I5a330K4oROymAhwePyF7BBIXR+rXM8kfejym3QV0WLiF2jPOweTxYCqrJHrCp3jiE1BEg7oGSE9DKSrG1FADZjOOkiLqkpIJLyxA9TbnVGkKBqaq+J12LHV6iH1FBGuV7tVAwL10drPIU9Gl0GJSUUIaYrGgHBAF97cIqCY8Dgdhq5fhzt+JopqILfZRG+EkptyvZ8/VBMVqAf+h1X0oehDNZEINhRCLhbqOybhSslB37ULx1bF1yRIi1ywj8axLkKWLUZ9/FuWp5yAyUtcTde8Os2c3r4b6q/Ln20KHh+H5MDD4fexYuexod+Gw2DF/jXx+/1cy4fnP9ILcXNl23CD979279aBVIk1Btr6dMmv/IFuN5bnfv91cvmKF7smYN080i1lCTodIVJQejGnNGj2rbEqKPPXqq7LovbckaLfvF8zLGxPT3P4BgcE+//Y12XHTTRJwOmXntVfLxjdeFV9srNz/+n/kp0fPE59JlWBUlO4BmTdPgjabhGw2kVtuOajPH/+0qsVz+WbyzF8NMvbtlFkikybpb75xcXqQM0WRkMWsZ2/t2UuqzzlONEWRwMDBUnTPExJwhok3OVbW3JMp9alhUpidLJNeGCfLHrtJvy4iUvTK7frf8+ZJMC5Bau5+UOTUU0USE8UbCMme734UX3Tcwddl3jypi46S75bOkulTPpUGd6zUpbSR9bd9IZUdOusZZIMBkQ4dJGS1StBhE3G5JNiYzfW3vAa/J+DXIXs7Gr0ZfrNZKlvFypq3P5XN3dpKdZhlPy/M4lP0rLsBV7j4rdY/xNMiIEGrVTSLRTydO0vFLWNkx7plkv/huxKMj5Pnv14s2z5+T4J2u2y+5RpZ+Pojutdj1CiRTz7RPW/dujXfH/9DGEHGDAwM/udZvexTois+IePn8Qdv3KstaKm8pQBcewN5ffaZ7hE45RT9FVTT9FgTPh+8+25TnUnHDqVv7/6Y4uP3r0vT2PjRB2wd/x5+u52SMdeTt2YlAYeDyBVBYs8/FzPQ6tHH6dApB6vNxtB2WTRoIWqjwqi68NwmTUcgwk31Q2P1en+tz4dRLiJNWVIpLUWWLkVEqLPZAVCuuBx7kQesFkx7dhD33IOYGuqwlFaT/WI+ap2JmEA13ctX4a4pR0SoqtqFx1tPKC2NutJSRNPwnncxPPoo1NRgq64kJcq5f9/2BkxLTSXUtQ2jeg1laGpHTEkxmLpmknx2NH4lhOZyUfzjF3i6dEBVFERRCKmq/sa/V0dygLj5V0ObN6E0lYccTiTs8MIx7FtnfmY6oir4TJFUfzERf8hKQZssNqVn4lMtBEwWWu3cgd9uRwIhAg7z717lorC/yFT1+yl66G5UVcg97TTqamvYogYJ+vxc+vA1pF55Pf7uXSl+4BnyY7vqQeRyc+H883Wxc2LiX1frsZc/2xI6XAzPh4HB7+Ov5vmYPGOC1NVVNhfk5kqDO1JkwIAWtR1lHTu3WO7p2K65PDdXf4t3u0UzqVL1yH9ETCZ9u8slMmGCSGysFHfvsX9dIrq3RFFEIiL08NXjxjWVi9UqgQ5d9GNuvFEPad3YfkXnngfXlZsrPne0eBv1Iwf2ubhzt0M7x323ZXcSsdv1vjWG596rl5CICJEBA0Sz20VzOSXUpllbEbLbRQPxRkVL0OWSQHa2BC0mCbhsUt8jW0IOuxQ8/bTseeklCZnN4une66Dz9Ee4WxyXgDtMtL3ljdd2bz9C4WH63w6HNKTESWVsrNTGJBzkBfj/aCaCqklCJj2cemmryN/c35PSSoKqKl6HVb47tbvUx7ll9aiR8vi486TBZZaQXRFNUeTLE2+VBptLCuMSpSA5VRYPPkkWnNBafP9Pj4c4HKIpigTDXSIXXijSs6eIiPh9Plk//2MpGd5NZm98SX549HJZOaifXPzZVzJp4fImXZKIiLz66v9USPV9MTQfBgb/UETkiC6V/f8Q8IWYufbnpu+OwmLis7N48ZoXOXHuNFp98g2LE7MJ35PPsd16M230MZxWuAf7By9S5CjEUlhKUqcMJl9+Ej2KhLAvJlBw1ijSstJxKj7CV9Vjf+p5JBRiw133Yq6tIeLzL7C078DKC+6gW+FmrOO/oNKagohgW7ud+Igoat/+Gvv0r3E88xzV2YNQSwpx9OjP8gtvIa14E/YdWykZPRLLog0kdOzK/CuuImfbYuwT3qDIqnsmLEUlhGclM3nceXRflEfkJ6+zNqoKZ0EJXXPa8sMdwxmwup6wz99iR4IHe34xbbtlsubi3nRd14DlnReoDOzS6yopw52Zwv2n3sDpq1Zw3NfvE7A7CcQn4CzYjRIMEnKFUfjgQyjjPydp7lRqbrqSyHseQURlyckX0e+b9wlqISp69mbFI4/S9vUPSZ49mWceeJALv1tK/MpN1JxyNjU9jiXv1pdIXP0TtrxNlJ9yHvaJS0mur8db48EUHol/3FOUhtyYiwtQ2nVi6+3nM/DiFQRvvRVrMEDdsGNxLV+O5gtQWVlL8TmXUVpeQeqOnbTavgZUM2hB/DY7Fp9X9wY0aivg0DQTe1G15hUz0buqf3W/vYHHtKpKVE3j1RvPYlO3TBKtc3CphQxu66SuUwLWilKKfEkk+Lcz94zzyFizmvlZNTR0vZQKdQ+dF9yPpb7hMHrYjKYoqMFgoxkCfPIJfPEFABarlY7Jx0B4W47rcBPcC7WfZnJ2/G5G9B6lZx/ey5gxv6v9/zWMCKcGBn8TinO3ExYVjcv928sD/xf4eepkBp80Sv+Slwc9euAFaJXBzpvvpv3rT6NMm6YHUxo2jLLuHYkN6QG4WLiw5fLPP2fnLdeQvmYrUlMNgh4b4fTT4cknoV07CAtDampR1MZYFBkZer6WsDC0M86C/ALULRugb1/YtaupHU92Dg4LB7Xv69gGmzmsubwxMJgMHYKvZ2fswYP7XNUtHbcW2VxeWwvXX4+2aB5q116wdCm8/z6ccw78NANtxAhKOrehbpeXjIoC/OEROAf0h+nTdQGmpsHnn7OmPokut5+G8swzyJVXAo3LTxWF2qRkwjtkwZo11Cam4Vq/mpduv4iOw8+l32XXElFbjdTVgdOFkpykj0tjn7XTz0ItK4I9e/TcMb/8AsOGEURBS0jCGuPGW1yBmpyItbKMUFIysnAR5vnz4IorKK2rJy45icryYiJ27MHkbUBrfBAHzWAJtnyPHBg99DcfViZd0KELUpsPUICgycyyM4fgKKsmrqiCp++8jvaOdqSHmfGsmsUx305l4U2DaNv2WLIu/TeT77kSs9oFbyBI7JZVFIWSOOv9x3FU/f64GorJhDgcNHTJxNWhm750uzFYHHl5cMcdMHEilZWLcLw/Fbvf+ZcKInY4z29D82Fg8DchKimZysK/RoI5LRTCZDrA8dq3LxW9++D58nOyXn8a5fLL9wvA9ctnr7QYmGt+Y7nm81EwfgLVfi/SrRvBuFjqRwzTo0BOaQxslZUFCQkEsnP0SJKPPKLna7n/figsRCkrRomLgq5dYdiw/dopfP79Ftsve/HeFgODhWKiKPrk+RaPWfPhrS0f07MtMns23Hsv3HKLHu/j5ltQRPjooiuZ+9AFKFYLltjGXB45OdR36gHx8XDbbaiqAq3S9SBUoAc5U1VwOmlITNSNh8GD2fboa0hmJteP/xFzeTSWSy/SdSZxifhnLNY1BgcGOoP9tTV9++LvN5Dd9z0BGzZQe8Io1F07QQRVAU9iCv6330YTYfHxJ8KCBYTl7kKtryXYGDZdYX/D49fiYrS0rSUEtTngl6JSkhKNz24nqJoxhYL0mD2PnLlLSdi+g7sffZzWi16kLdsoJBzX5iKqN6qo25exp1s2bT7YhTs3lQ9jMvk2qx1LI1dTbbchQNCkUhP5OwKb9emDpKWy+6X79Lgo9fVIWQnl5fMoC9tB1bv3UFT0HQ5HK+y3PfKXMjwOF8P4MDD4m2C1Owg0Zs78Xyd380bS2rTdv9DlYvMbb+EpKdU9EmPGHGJgLr18q13liauuYuytN1AT8KAoCvnPPKgHE3O79Qdmdjb8+COKFtLbOO00iI7W/05KQmITkeJyPdPs0qX7t6MoLbavIC0GBgv0zCGkBQ4tyFhj0C5QkJBP71dcHAweDFOmonRO4Iw3xnNcj7PwtW5L7rc/6vu3bUv+fc/qxkZiIkp2O+Y//63u6bFaqTnuBHj8cfD5sDTU69cA8CWnYZo0EVt6KkPP64fjmmshKwuta0+U2hoIBvfvczCgBxPr1w/eeQeuvx5cLvwff4a/vApqawlbPAe1pBj+/W9YvpxSsaC88y7fXHozGwedQl73PhRldcDbKROz3U4gMpKgxYKgNGWt9UbH/+o9o9hs+0UNPXByUVQTmslKTUQ0WnQ0opoZf/MjbOx/KibRUABrmRclJJj8Gq4iD/0/XsaSOS6slX344bpTaN9QxYbZPn6MT+HDHp2o3zyXx6ZMIKIkg9zofnx7+xjqwl3sbBNNg9OC32Jtat9rtXIgygEfLBbIyiIU1JcZa5deSN3zd+B29yI2diguZ1vi40/Ebk/+1evwd8HQfBgYGBxxinbvpv/xB8Qp+Plnep17Fs7163VPRmPsB00LYfr5ZwaetQXZtAOZOLHprZmff2bg2VuQjTuYeP9ZrMl7j0wlE5Pdgfj9JJ57FbI1F9+ZZ+E542zcJ4+A00Zh3rQOMZl0D4fFQv1b7yF7SnFqGjW/rCHyvv+g1NUS6piD78rrccyeQ9yGC9F2bqNw/AR8FZVYq2tImjOHqA3LkJ2l+N97h1BRPsquXdhHnIS9rpb0Lr+gaULZmy/g3b4Sy5LlJH71Ff2nTUY8AWruuJ7aPcsR0TAV5JOwbAtav+4oG/Pwj7sZ84yFBAoWYMurJ81bhWn4EHa89AY//7yMyDYRxM6aReKK9WhFuyl7YTyvzduG1G7FVLiOfn4/9iWLCC2aRygigtKIaGrefJ/k2XNot/EUJG8LDW+8RGjnZpT8PYQtWohaW4c6cyqa2Uzpg0/j+WEJ5uICwtt2YttTjxE1YybOjz7Ec/Yo0n+ahXPYQNptWE9l1y7MeeEVuk6dRdy0HzH37MWs82/nxIKFDF41k65nnUG008T8UafR7bW3sdbV4Xc4cAUClCakEFese+wcFSW/es/IAYb1gZ6QEILZ72VB224MyVuDYg5xzWsPsSPlWD3uyAGE1TUgHoX4tjNgvZ1vEvvSozKMFb3DWR6TzOm7fsQckYopqTXnxL1OmBW+9riof+g2hr35OT53PV+e1YOr35yLSQtSGxGG1HuxexpaDK2O2Qw//4yEgoQWfEwwWEvZWW1ISLi5KaeSxeL+r7+bvxOG5sPA4G9E7spltO7e62h34zeZPflVIlOSsRaUkHXxnfhTE7DtLGDVmNPo/PJXBCIjaYiJZsktV1PVJoNBDz9F/MoNKDFRmGpryb/vLnzprUh66nms6zfhj4vBWlvLykfGUTegO9FFu0l94HEi12+BaD3BWdGV1+P+4RPCdhYjXi9KIEQoIRG1rhbF7yeY1grzju2IzYbi8xHoeyzmVctQVAUxmfBrgsTFYN+1i4bWrXHu3EnQ4aA2Jp6oonw0dyQSE43v3LOwTJ9JUFHZMKgfPZ94nmB8HMGYKGpGDCX6oy+Z+9x/6JlXTPhLb1O6ejagYFm8nKizryDUoyOmwnKkfQbqig1IQjTKxh3UpKfi0FQ8XbpS9uyzROzOI2LsWFi1BjU+AaW6im+P+Re7sjPoX7OCvm88Q1F2T2JqSwhFRFJ6ymnU9juG7HNOpyi7PYlV1YS65uC/7y6cI3XtjRbwo4Q0VFeY7o3w+aBNG2T1KpTOXRCbDamqYu7DJ3Ls5ztoePs51j15F92/Xc2mn2fgFoWk4ScSSE1hzr+H06fLpbhHnkjN008Tfd55aGkpmNauQ+vWDTU3T58KA92rZDIRMmmYfBqhcCem2gZq492El1QddP/sFZD+N0p6DGf+DXdxwr/PJKzi18Wo2884n7icEi7ucR0nLtlM8vblbEtozWVxy1lSNQIlTmVP0RY67PJicYWxxurDk2rC5DuRoMlFW0sB3T74D6aQUOxyo3ms9Nix9uCGsrL0fDbAitmvktKpNfFxJ/5lBOKHgqH5MDD4hyJ/WL7NP5eE+np6jLiGzuPex6KYcN1xH+aBg+jy3RJ8Q3oTnpJGwiefM+rNT7i4TXfSnW6qe3THnpuHJSWVjA8+JatHPyKioqnv1oeIHTuwJyfT/5WXOGHYyfR84GniNmzB0zYT+848LKkpJHw7Ec0eBscMJNChDYrZjDkpESUtDUIhLC+9SMAdhZLZHqVbN6w7NqEOGojSri1qr56oJhOOOXNQIiJw2Wwoxx6LxWJh/X3PojidmNJaYf7sC1yfTsS6ZDn2DZvoOuFLFKcLS2orHJ9+ScLU2VgsNlLqHbiffQNTWQWJJ15M4qrdxMS0IRQVSeCnuajzF2OavxLlmIGob01AiYrCM6QP1gXziZwzi7Zx0cRlZmJzuajt1gvLts2YkxM5c9fnXHTjyfS74FQkMYn1b32FdcF8HLnbafXvW+kUG44aH8fyd19Anb8Ay6zZuKxhKP36o/Trj79Te7DZoHVrPaKmosDLL+ONigSbDeXrr1H9ftJmbcLkDxHWaQDZM1djz9tFt/nLyUhtg+38iwgrLCWoegn6S+DyS4mdORN1yBDMq9ag3DAKU1U1/uxMSrt2QUtOwe9y4o2LY9mL14Kq4jmmB8FwF3aPF3FHIqpCRUI00hjnQzvggR00q83xPxqnP+JXTOfYR67F4vP813ux9ZSvaNgm3Lz5Y6r9HuYkn0K/sL7UFZxEZPRkvkxZS3k/D5tyjqG8bRzBdlHYEhIoT0pkfEQ+L3S0s/b2HHIvbIecHc2b/U5D7Hrslf3imOzYAeXl1FVWgMn7tzM8DhfD+DAw+JsQ8HkxW2xHuxu/SU15AbawMF3P8Nlnuo7gueegMTS2CHrSrH2CgukbGv89KIPqPuXl5U31Brt1J2zHtqZtdfnFhIJmvPnboKIETdEIbttMdXISoihUP/M0ir8xksPOnVBWpmsp7rijsW+NHUhMhIoKXRchgqW+jqZEX/v0WRHBVKqnf+fyy+H552HrVigoIPOmm/SVLmPGwMiR+2WK9Xq9FG3ahLjd+goIAKeT8CWr9e97g3w10vT4GjUKpbSUuNRmIaTA/oHB9qWqinJnOGvffq/ZZA0Em/sbF6d7PlJTqWqdrJ9Xo05lw+guiAgyaCAzProZHn4YbrtNP/b++2HtWra7OxKwutg6tCNL+2WQV5zHgo+vozrRglZUiK+qGovFTmHXwZi9PkyV1VBqQ8LCMM1bjVrvwdTgQ6mqpjbKiTmoIqoJMVvQTGZwOBBFQVMVzEGN8lb6ef8wdETT+UQU7cKn2g++V/ZBDfhI/Pgnkr7ZygbnVlJsk5hvX8362MU0WNrSeXeIGnMW3uBWvI4SMtNmstLVl/hKP939cNf6PSi709lgbc1Wdzd62FezrVNHPbGcyYRmMiFAyGym6l+3UpK3nR6Dbv9HGx5gGB8GBn8birZtIald+6Pdjd9k2+rvycg+vrlg4ULYvh0WLqT2pssJW7RGj4Hwzjvw2GNNYszo1asOzqD6+utErF3eXP7UU3qdGRkEb78DU0N987b3x1N4ck/s63dgLa1FCWioVjPuRYtRXS4iyyswNzToWUNrauDii/V5+vvug5UrQRM4+2zYvBnCw/Xlpikp9Lr7av3BvLfP11wD8fF4p8/W+1JXp2fB/eQTXfBqseBPSdG3lZTAhRfqx6emogSCuEaeQtyoUdTee6/uqk9NBZ8PU3UdDB2qt7GPSDVizfL9rwvox/j99BpzZrNIdK941OdjwFW3QL9+5L36OssuuARt2TIoK8O+cj1KbWN/V6zQo2medRaJyzfq16VfP3jsMbr3v4xf7hyKCETZwnWBbGLifuNsd0aRntiOTl1GkN3lBIJBP+1PvB9v3wvg33ciBcUQ8JE69WPKhw7B7I4gctVG9vTLZueVp9CQHkPdjWcSjHUSVtuAvb4aRYHd3XuzpVsvfGhs7piN3+lCTCZid+kxVobPmo4CBOxWPN0TCQscWrbnDos20jvNSd/ASWxMGkmevR87GpwcW+hErViKP9xPWXUai0pOpnZ7Pil1S+lv/xhfzW5MSbuY6u3CxsIAbdumEda6DQCBYcPwPP0UKAridmNWTbTp3vuwfi9/VwzBqYHB34TanaV48ioPad99Yycc+P2w4ir8F7RQ8KDltL4GDyvmzcVUOpmOP84msH4djtoadj77BKbqGizT5lLQPouiux4ndsUsHB9+QEGYE2thIQlZXfGOeQPnuhlY3vuGaiULU9EeXFk98d5yBra1XbB+NQH7nKUEc9pj35QHCLvra4iIjKDijedRB8USiAmj7vohhL+5gJDHx7zR59Jr5o9sv/V2Msc+QnV8KvFiomHXVszH9kLyd7H75LOJmTqdqc+/xqirLyNqxzbE4UIrKiFvwCgyNiyl+MGPcf30LfbvZ+MqLsY6vBeKP6hfw7o6RFEI7NyJNRCgztuAXdP0IFNffsnGay6g7MdPGFDXQGmoipg4N54fvmDZK/cSuWQi3aurKMvOxI2FyqefZXFsAgoK0SuXM6i+jtqaIJbwaOrHvkhuMBPX+qVkVVfjjWnAExFOzWOPkltRRM9rbkRUBXNFDVowSLcbrqZ7eTmKQLC+BswmfA4H5pCGRdMQj4dtxw0iobyUsK1baUiKx3LBuey+72aCaU602bNof8YqArlFTHvpAXZvnNE01tuqfdw2ZxFmdzTJ7gjCXnudPnnLqPevZVdOByIfu4uw9EjKH72KsFGj2HT+eUz3FrO2XTwXBhTcS5fjvvVqQueehWnEleS/+zRRe7yULlzMupEnkjZBxRnUqM2Mwdd5MOGfP4vq91AdF05cgQ8JCo6VRQTNIQ5eh7I/IRVMmkb/HVVs6/I5RTVR2NYVsnFIL7oF5pBY7aK03kyZXyGnPAWT+2d+kWR67BhNeOhTtizLpiEznB5xuykrdJB0zgUwZQrWadOwAtpDD6OkphI2fvz/4xf198IwPgwM/ia0btMdR4foo92N/8rHDz+N5rGg5ZbQkNWVyIkfwbnnklFUoS8Jzc5Gtm8n4aG7sKkBqK4m6YUXQQNWLSL41BWYLUGoriby1bGwEVg1F+3RQlSLQ592OXYwppdfho4dEV+QRNWE5YknCb/matadeD6iKJhPHoM6ZSumjbm0H3UStm+/olf3HOjZHdPy5di7dEFdswxrTi+2npNGZ28Cvsoarhw1XH+LDQYpb9ORWLx0mDMJQSH9ztMJPvQI8vbjYFcJ3Xgb6tgnmpeGimBtzM3iLqtACw/H5PPBiBF0/HYWXHQdxCfw3UevMyY8G0dWFgkRvSE5DOITeH/SDO63C2FZWaSlp+pTKQ01eNwxhK9ZBHv2YM/KIqZvBEQkI/FxBF55EUd2B72u51+AAQPA5UKOG4T6wEOQlExtbCzh23dgnjWH4PFDcdY3UJHVnhizBXbtImvqNOjQkUBZBRHLV8HQofT/4Dt4+WVo8OFwxGJJVBg1YxVcdU/TWP+8ZRttEuL5YtEyjk3uieLKIclppaRVOxxmJ23c5wFQIK+RePIYEoGyyT+wQQtjaHYaHuvrODJGAHn4W6WTP20x86++kbM/+JDer78GQ4+j+sKLqdA0Vh93Fj12fk+7uStZenkrBj1fhd0bZN6pI+g2exnW+rKmbLgtYdJAU1WWpZhYsHkEOZ4glrTuZOQW80lSP44tmkHnMidV3jJKWpexkkQSnSG+SakjsyqCmqRqrt0+lW+zbuCZ4h+gd284+WQAGpYtw3LrrVgcjj/41/TXxph2MTD4GxAs92CO/u9z20cbESFM6U8faUvaAw+g7vWKpKXBzJm64XDiiWC3U/LKxBaDczU893mL5cEXxjaX79VJ9O4NqorvrdfgyScJRoWRtd6PrdpL+Jh7UFdvQr3qKkyff05NSipVH34EQPGJJ8Mxx+AZkAObN5P+8lTqpn+FGgiAy0WgV2+8qelE5erRSjGb8aS0xdf/WEzjHqGhdQQ0BLB+2hwSW/b5AJhEUGtr9cBejz2mT/NU77Mio4XkcmFmU8tJ55R9jvkVbQexsfDmm7qG5eefsTz5FPj9cPbZWItLdE3LtddiavCCphG9cRP++jp9qmXjRli8WD//O+7QtTWFhXryPsDkadA1H1Om6GPYiM+jCz1TXfGsySulrMbL5B2lfLO8luWTPmP13PGsnjueXXOWNR2jlWyko6mc/J1zKb5mYPMptmuH8+6bOKN7DrOGjWDl2Ed5pqSa/CeeIOg00dA5ipXDR6GpKtpCKxsy2+CzWWm1ZhMmh0bIbKY4Ie1X700AVdO49oZJTHjsPzzx/KVc8OyVnPfWY3RdtIhUXwXbYypZb65Ei6vlpA1pdN6mkRwTQOtoR2kIp67Bzb/WlhFx90uQkYE2YQK1P83GHBeH5eGH/9YBw34Xf2KOmd+FkVjOwODwadhYLpqmHe1u/FfqVhSJN69KKr6YI5KbKzV9Bjdv3JssKzdXgqeeLnnz9oisXduUeEtyc0VGj5bqWTsPKtdOO10Kb71d6m8fKdK5s56EKzdXZORICTqsoiUliaiq7H7/USlZ8r3I6NGiaZr4brtY5O67Ze6UaVJZ75W7522Rqav2yNT3J4mISP6UV0Vyc6XohM6yu2KtfP7Zm6K5o+STd2ZJMDJSNJdLxOEQGThQZj/2nex8Ybx42rSXdecNlWBMpASef+bgVO5mswiIt22Gnmxsb9K7G28UufNOkdhYKejRqcWkc3t69W4x6Zw3MrrFpG9abKx4sjs0l8+bJ2KxiJjNegI9EGnXTu+HooioqmiqIv6kRNFMJgkkJIhYrXrf+/TRE9jZ7c1J5AYNEklJkUlTpol06iSSlrZfwrM5m7fJrqpqqa5rkLGfzpGpSzZJg9cndfUe+WH6vKb9Nnw4vunv2k9fbPHeaaivlRemfiEvr1gnuyr3fzb89PCzsuqGB2TujSfL5n5t5dSHP5QVGe3ls1PPkMIhXWXLsSf+7mRwDQ6z5CXFyBWvnyIXfTBKvn5pojz33gXy0COnyrenXCLrenSXNZd1lCvvekSWPPCA1M6ZI7U//yy1P/8s9UuXSqi+/lB/Hn8LjMRyBgb/QP7X1fP1eTU42roJ7NyNdt2ZKLHJhLp1I3DnnWhnnI5oIdi5C8e8n3FvOxUtbzMV4z/GX1aPpaKBmDlzsK0/A9m1ifo33ie4qxS1oIKwOXNImDJZzxPSOp3awccQ2b07Sk0NqqaBpxBRFBJufQJzbT2a00moW0d8/dphmruUoN3FwlAsl4c7UKqXkV+2lO1L0pD8PZR7ZxI3ZyO1Iy7kuMJSpLqa0164FbWujvIPPsG6fQfhjz1C5uYxxFUWMW3osdS7UojKqmDDpg8YdsA1UIJBRDVRcMudJDx0L/aqGtbc8S+ce/aQd8oIOq1Yxn0P3cY1q/NxT57K6i49cZVU0q1vf+595AFu+GUWkd98zroOSSDg3JNPp/bdsD35IY4ZX2P+4DtqnZ1QC4qI6NibvFtyaLWjAtOXL1Lcph2pLgf+LlkErXGEz5mO5vGjonulxGyGYBClohI0DVN9PX67BUvAz5ZrTiNz+TLdW5Ke3pQQjYICRp19GuLxsf32S9h1bjfInYYARUWbWVN1DFGOMDI6xbNmz1zmfLqI/h1TmPnDFEo2LKJNSg6BdQolj8ygoLsdZ4OH0+Y+s/8qJ8AmcPrOZEJ2IbByF9tCQsmOGmjrYJOrLe1taygJ78uG49pwck01vj7dCIaFsTplFJEVHpJc8yiMsdNuV9lBeqf/ht0TJM1bzsMPzGDxqKFEy9fk94pmYVJbesb+gv/kNBzOFF5ofRzW1m2wpCT/z/8O/1cwjA8Dg784EtBQTP/D/+Hl5RHo3hN7ajtM93nxnnoeNZ27sem4XvR+4Q2sV19NKCODhjHXQ0MDSmUlZp8fAgFsTz/Jnt7H4tyeS0xNLSUpQlJ0DKZPPqSuV38sO3JR6uvxhIVhjgjHl94Kx2tvUN+1E+LxItt2YLHb0Mxm7CWl1OVkY928A3bkYduynZDFTvrkSWzq243pczbgc0cwr1UPOuzcxrmaGd+SmUShUF3pIaG4DEU0xKSgWa3w4dvsuOAsuno9WNvaCSrR9KqqY+4xbYj74iuGLqxvugReqwO7vzHehBYi4183QSiEmM20WbsO645cwocNI3HuLzx4cx3JhFBrqgm/606UCCfRAR9vHj8MU6cuWFesIL7fADxnno518y7iVs6Fu87Tc9TUVGMddy9KfQOWRbNoW7cTKzaoribYOg5TvQfH+u1I3Wo0BUJaACU8HKW2FlRVXx7cGElUfF4sIQ1FIOu5j5GQBpmZ8NNP0L69vhLIYkFFQRkzhnYff0W7ZyYAUPH110RefB81qWG4w9NQxo5lfd+hzFsyh9NPPp9jYqOIqi9mx/VjqW43GOkYRbpZwZuh8cCePdzZ+zi2vH492zpB26hMehz7JK7CWdQH9OW0Uusje0hHojKjsBQvZ41lJ46NXnpY6qnJrmRPrZtdbcJZnVzLiPmpKCGV1vkVgG50aHv/VUD9DVW1IpBS6uP0L36k4KrjIHga53WOxtJvJGHte6Ja//eXt/8vYhgfBgZ/cfy7a7G2+h1Jro4QPl+Qik69SfplGnz1Fa1GjwazmT6L5qEEQyhOJ6rZTOSLL+F78GH8ZgtXvT+Nj9OthB/Tn27hAkmREB/H3I8+4cKoMMxZWTjinZCRADHRFGV3ZNptd3PVWaPwJSTgmL+YkN2OuaEBpVFpKIAlrwCr10vQroCm8vblV3P6xIkMH30+Je5IPCGFzHP60HH4SWTc8yLBqAiUQBCH14/PascaDOD65lsYM4bYn38m9sZbITKSogFd6XDHoyR2ySE6J4fdKW2w+GpIyd0JKFhDAfzOMKwNdShAXfc+KF4PrugIIhYvhXvvJWbWT4Sio/ju/YncEq3AgAGkffQBnHceqCrbeufQ7sd58OijxN77AFxyBTgj8bvdWH+ZqyeNGzAA22cf6Etv4+Iof+9JkmO6wYABRC3NRYmMhPJyxGRCDQYJxcYSqqjADEhEJJSWNOciaduuKSInW7YAEMrNo3rYcCL9fvx2B9jszL/kAo6vrtN1K+XlEBODGmYlkNmO7XcfS4/+d6CMPosOixezyvwLDB5MyR03Eq5otL74SvJuSmC9moxZ9TDrh4WI1c7thd+SGKiia2E4u9dU0cb3E+WeOLKGpx90fxUX5TMlWiWmXx2xuysZdNdUSmPD6eG3MX1AD9am5NHbHKBWdYEFqpJjaLVpJzuSU7EqXhJLyzH7Dg6/vhdNUVBFUOuFtOdn8d+VIwaHiiE4NTD4i6P5Q6i2/9H3iLw8lN69SNy0XBeALligvzG73fjjYkDTYNIkePZZyM/HZjFhNZu4JjuEWpAPJtOvZ1bdpzx15QrGPHAXlkAAa3ZHTIqC1WxC1TQUBEXTEIcDW1UlIYsVs1dYfWpbTvniYwKisadDBHP+fQ+J5wU57utfqC2Ioqp9d97vfxEoCrGVZdh8Xt2CCQ/X44nExsLkyRAWxuqBKVRPfw8lLp6elXnkhYchYW7qE5JQENRQEIvPg79DZwgLw5G3jR0f/aA/rGNjCVxzFR8P7MX6hNYkLyhgzcTPW0g618i+MTVSU6nv3Kn5uhyU9K65vPzSk6BjR0hOZsfzzxKIi0PNy0M0/dXfVF6mX2/0gFi19TV6NFGTST9PRcEUDBC9dROm5GQcngYc1VUMffEVPbia06kLUgF3ZicCSVFYhoxBrakFEUyqSrTVhi+kJ1WbvXk5pf5qSiO30NlXyI45XxPpqyNj0xpy1GmUt4nl24paPlIamCYF2FNjmi7BtmVbmDt1Pgt/WkmedGZ0SQ7ReZ1YXdsKs1fDalFJLyziqkk/cNOLk9GsTuxaELvfR2R5MaoIFk0lJb8c1S/8uukB6j5B45g27VDvfIPf4H/0fywDA4O/A1u3lJHQtSfWTWv0gmefhagoGDyY6tgw4t+cAFdeqT+EU1MhORnF5+PY/idBfYO+EuTuu/UVLD4fx144msC27SiTvsJ84kl6eSCgPxi2bkW56y5su3YRiI5g7WtX0fHWd7AV1dCQ1IZAairhy+ZR7QzH7a2ny7QdWOsDrBp5PFU7aqipj2Vh+J30U56g+/AeMD2WLukuFKsVOnRge04nssd/CD176tFPv/pKn36YNIlTnvwKd14lfPUVtnCFHldfhWNnBQ12m64BuPxyvEmtYeEC/DYbpu1baX3RSXrE0wsvpLgyj9mbG7hwxzoyXrmeiLwtev37kLJ0re7RWLWKdy6/jp8++5AONg/3rFrVVL7fMT//jHnzWoI7drH29f9QKU7CystJBjxFRQR69sQxfTq1sU7cJX48dhu2Bn1qSAmFsBeWEDKbEUD94gtCDhsmr5/y3kOJjLJgmf4jDB9Okb+e5PR2+qqYdeuamncuWUPrURch6zfj+fJLAn4/lT4Nde48Wm3eSsedu5h96Y3UWtoQHdYacdbgz90K4YMI2heyvW4Iwxry0aJ3cuY5t+BLSsTn87H4hP6szhjNpoRwulp9RLsCZMWWk1ihUFXWjVr3NN6//wYuefwz4rdup7BdJo7CPVgUweQLYAtBbWoyVgWKk1uRULiTgFnFGvh1E0QBPVT6zTc3eYEM/n8YxoeBwV8Yb0k9ZXnVWH3NSbbqK7xo+/5Hule8dyhCODlAjvdbh4j8ar21ngYmrVvNldUlNLTryPZzL6XntjuwVVYiX39DvKb3WfLzwaQQyHASmP82ToS89u1I2pmP9933+PD8y4nYU0q/nn14a9yV/GvBFso+eoJQ0m4iivwkZ6dR41VoGH4errULiFo1D3t5FR3ufRfbnnJAxV5RhHP3FqpTE4jOLyHotGGpDzD3+I6k5hbQdfMGBv/nPBSgwRVBqGMnFL+Pvsp3SDBAcP06OqxYDgKF0S6cIR8lzz/KmgtP5fTqKjwpSUS4bQSfuZOyE9IJDuhNm83fYoqMJGS1UrtpM7tHnUrbzyeQf+65pD+5mlJvgFBMPCU7ipi2ViFbA7XBi+rxU+2OpvjZl5mWmA1A0orlnFFbT7m/BHuUg4FLfqQkcQCuymmodXXU13mxRLjxPP4M+dY4HGtXkl5VTUVqOpaYeJxfLGbl3fdzTN69SEM9HZ55HlMwhCgqzjI9AqgvyoK90fgQRcEcCiKKCgjy6cc0JIcRlucjZsksECFkNlG/ayshdziFt55F7JnXIyYT5WumYlmxnuiaGjz1PkxuN1XPPceK+ETSdzdgqq1Ds1bit9roOm8qW8M1/HW1XNS7F5F33oLXbobPQqSclM9CZ28sSk8Kk5aRlL+JumgH/T/4guLTSqnqfS5pkf2wl+fR8/5XqGibSHiFD7W8gjsvG4sS0u+vxB1bUYIaqhYCRaHObsdSXU58vQ+vzYGiCWbt14Uf+93dO3f+xg/C4JD58xffHB7GUlsDg0Mnd3WpVOVVS9XO5k9tYd3R7pZ4/V75bPIPEtq+XfxhDtHckaI5HRK4/x7RVJNoQ4aILy5GNItFZNw4CbndEkpIEHn3XRG7Xb7JnS+ye7eIqops2aIvE7VaZU/vHJFevcTTOkkKdiyQkilvS8isii/crpfHJ4j/0kvF53DIihef0ZeHms0SslqlOrO9BCPCxJceIyFVkZqwCPFZHVLjDhN/mEUKumbI7MGDm5fARkWJ99prRQOpuHGMbLviAn2J6uOP631zOkWmTBFJSZGvc39pKsv/YKwUP3ydBKMjZduT40ROOknft6xMX1J87bUSSk6Wyb+saq6nrExk3jzxxiTIi+8t379cRGTePGmIi9H/3r1bQg67zP/mbil95S7xxUeL3+85qC5JSZF//zBLvpn8vQTsdtkwZYpUDx4seUOGyqQPvpOaWLfUJMXL5qRY0UD8DosErCbRQLwZSbLnmlNEQDSnU4IRNglZ9GspMTH6v23aiISF6dckIkJfrjt8eFN/Q4lJUrBgXYv92jb+vf3KV9x2q+x87hkpbt1Olr18lyx7/QmpSEuQ0c++JG/deY2UdMuWTT1y5IKnX5MdadGyK9Utw967TeZcO1o2XzVSvMfnyI7lj8iiO4aLBuJzukQDCaj6+VRFROnLZyMipCheP9+Q0vLy2tAB32Xfj8l0dH5QfxEO5/ltaD4MDP7CKKpCZHoEka2aP2GJrqPaJ78vwDfTZnPaCUNQVRVfhyRC3TtRc9FQPBM/xG+zUNO5PaGMND24Vbdu1MXFEVAUmDq1OaR7VRVERMBNN+nf4+L4ZsIz8PXXWIIqVS89SYmnAl+0i3c+eoqNL4+HkEbBrjK2ts1kqy2FTZndCNqdbLzjNuyqHy3SRJ1Fwx/p5suLx1AXE4bd50Xxhgi6Eml/4aWIohCy2qlq1ZmGSV+C2UzUoiV426Trj6Crr27WnUye3HzijWXJ+WbihpyHYrVRt3EFPPlkc2CwMWPgootQgJCmHaRfsdnNKOnhLQcT26cdNTaOYzZGEtv1FBRFpba2tEUtzKmKj9MyWmGOi4PPvyAkQnp0FKnbqzC5YrFU1xEa1A/NrOJPi2fzhecCkD9gCFpSb0QBLcZK1VvnowQ1JBhEQgG4/XZdYJqQgBYXpQdIe+klWLOmqW1FgZA/8KvnEtq4HC3MQeDf19OqQxzmLSuw2uyEzJ2xl0WihlzcXllKTvdOhG3bRatteXz80L+Z2e8YAmoY7Uxupo5Q8WS58JR6sKyuw/x9PpqiUNaxC1vjW4GiELDZyevUDkVVUZKSmHfyMAQFr8NFZYz7oMvboh9vr3fPWEb7x3EEjKHDwvB8GBgcGlpIk7x1ZUe7G3oQrKgoPXgVSMBikZrkeFn02O0y86NnpKxPe9k45QN57YLh4kuMlfrYSLnhnZMlGBGuez5UVXa+845UPfaYyJ13ihYbK2W9cg4OtDV6tLyyYXqLwcde2fCjrJv6ixRltJPdS5Y2BRor79FDgjk5Irfcoh/zyCPS0CpJ6kYOlbzrTpGGEcdI0GETzaRISFWb33itVpHISP3Nt/EtP2Q2S9BmE0lPb+5bYwCwsl5dDupvMNwlda1SWgwMpsXGSlnnri0GE8vv3KPlYGLuyBaDiQWjo8Tbq1uLdVV17Lh/X6OiRGJixG9zyPI7npaQ0yU+d4RoIHUvXyUlNwzSzzk9XTSHQyrj46W+fWspW/q9+JPjRVMV8YY7JeC0i6YgpX1zpD4mWm9z7dpmD8feIGeduu7ffnR0032igWitMyT46ccSqq2R+i+fkJDLLJ70BNGcDglO/FpmPDZVHr3/Y9FGjBBPeKR40tIkpKqy8fyLZfHlN0r+hnWy67V3JOiOkqr2HSVoNovf4ZBgWNg+3gtFQiABm100VRX/PuN8oJejpY/svQdAD7JW9j/wm/sfxQgyZmDwD6Asv47YlLCj3Q19iWddHeJ0Qk0NChBWUEKvh1/G2yoF8BIVHcYZV19MHSFC7s7cFnMilZ1LyXv3NhJmrcSxcjq7772Cot3FJK7L4oWHr+esxfmET/6C7Z2TcZQU0mf2T5xy9kpCOwrZ8cJYqtfOwZJfSMefZnLaumUk7yphz7PP8e0nEzjrxjGoC+YTtWkT3nbtcLz1FruefILa3j0pcnsZ+uArRG2MwFZcRt05gzAvWoff7MbbaRQJXz0LioLm97HqpC5kbixnxg9PY3tjAn2+Ws6MOd/Q6rsfcH/zGVvOP5Oevbry5djRXLpgGZZvnqEycyvmPeVEHdOOZaPbkbC7I64PPiDProe/txUW0qZjR6Zfeg7H5m7FPuF1iqw1WIpKSOrYjh8uPJf+u4sJm/AeuVZAwFZcRHRmO8Jfuxf7D3OwfPEOdSkhLCvXE1VbS1m9j8iISKoef5ol1mhi1q5kYFU11dExmCIiqXriCTZcex29s9PxPXAtaz9bRMaWJezp1Z8tJ3Vl6MOvYnt4Eo6aOoK9emFZuhTfhA+pmTyD5Dnf03D5bZiLyqjv0olCaziZmh9ycohdtAgpr0Bc4eBpINB3KN67HmPNgCwyszLZOPoGkgpX4nrnLYpOHEmb1hkAKFJKREkD/r4pWMbdTVWnUjaun0h/r4bHXIPJaabiybuoueReWhVXoc36Cc3hQguLQnV7MFXm0+r2k7HmbSHsrlvQzDa8qkLg1NGELZ7HnggzCYUKismGQwuhaBpKTQ2K04nJHwTNh+JwQGMI+N9E03Th82uv6dmDx479439H/zSOgDF0WBieDwODQyN3Ten/s4JckdhYkcGDRXr1Evnhh99XR2SkaBaL+G0O8VltEjRbxGezSsjhkIIV86WqVZJo0VES7NdHgjmdpXL8x1L+xTcSMpultm+O+Lp3En/bVpK/bZ4Ufz9eNKtFdnRvLfXdc8TTprWs2bpKNk35QkJWi+zu3Fp8aalSe8pI2bFno+xeNEsahgwSv8sl/nbtJBgXL1VvvyWVmzZKzccfSchkEl+/vhJISxPPKadIaUGB1Ez+WjSrRYqyssWfmibFvftIUdccqevaS7RIt2iKIpqqyp4LB8vu84dI0B0phfN+EG9EmIRMJvHndJGKbyaJv0d3qZz0pWhWq5T0aitar56iZWY2vfnLyJESiArXr6/TKfL55/o1a9SveLtm6dd97zGN5dK1zf7ljcdoFrPI4MGi9eolWrtMCRUUS+ijT0VTFKns1lckJ0fEZttPW/Hi0rxmbcWKFVI5rL/uLYkMF3G5RIuMlE3XXKO3Gx4ufne0aChSn91N6pLTpTo+SUq69xRfeISEVFVCJpOEFEUPrx4bK+JwSMhk1u+dl18WSUyU3TW75fsJ90jAYpKGrr2az2XFCpGRI0UGDZKQ2SQSFqZ7Etq0ERGRmhnfiC8uTAo2fiYVq36SkMMmVbm5Mu+lF0USEmTXsJP0c2nVSqpjY+WHGZNkxrcfyY4eXWT3oOPl1S8mSVXrNPGmxErIpEggwil+h1P8CYlSl5ouIZNZir+eLUGbQzSQoN0unpSMQ/N8gMjdd4u0aiXyn//8/353f2MMzYeBgcGhMXgwzJmjJ2W7//7DP37PHqS+HgIBzD4PFr8PNRjA4vOjeL0kXXMLrqIylOyOmBYuxvT9D7hfeZ7opBgCUZHUTPkO63fTsAQg+ctfiI9oixIXz4zxd+P87nvswRBdJv5AVmQSalw8i169H+uChYT9NJfW9jhSE9rgsDnxZXfAsnUrpqREIl99DXdWB8JbpaNFRCA//oh5wQLsP/1ErNVKuDsWJS6eh668gfv/cydxa9cQ9IVQLBaU/v0Itk5FiY0lZuYqUtaVYUprReLk2Xh75lCf1RrLSScTNe5xLFdehXv6TJS4OJZ+cDvK19+g7JvwDqjvma1f33vvhdtua75ucXGUvXhvi0nyePG6g8sBIiNgzhyUr79G8ftQJ7yHajWBzcay5z7VlzFr2q/HRfnss6Yif7csuOsulMRE0qdPx+92wxNPEOjfFxCc82aiPfskLm8DUfm7sQ44BvWMM1AzM9EsFmqmfU9dj25UjD4dzWzSk81NmAAlJaT+sIBhrYZhik+g8IXxzefy2WewZAmsWoUSDEGrVnp/GxPqhdtjMJvsuN1p+Ob8jMfpZOs995PSqzt07958LnFxLL/iFkYu28LxOQMIN0XhlhBXtUrDZbfDyBNRHS7MZjsmRcNcUkx+fCIem526rz/H1649ABIIYsvP+6+3twZgs4HFosf4uOEGcB1dTdXfBcP4MDD4CxIMhDBb/sCfb1XVQfk0DgURwWdz6NlEG7V4AZt170b8G9Yz+/KzYMOG/dtJTaWuSwcUlBaDYzXtGxGhP1RvuglKSkhYvPbXs7eC/hAsLW3+riiI39/iMaX1dfRw2al3uijt1oPKm++hvqgSX3Kinv01MYm80TlwwQVw/fUQ5mLR96/BhRfq5zBmjN7nfft7iIHBpG9fTP6a/37ue8v3HtMx6+BtSUkomkbPm8+GU0/V2z3vPP3vkhKuG9RRD+72zjvw5JPkPXVXc5/uvx8mTsRus6GFQoSuvRbfqFGIzQZvv03Btq0EIyPw2O3UrFiJ3+OB0aNRXFbsK1ZiCQUxnXkqpmBAvx4bN8LFF8Ntt2HLaIvSr//+/b3+ehgyBL7/nlCES+/j2LH6g73xHBV/EHXo+cTf+RiuTz6j16cfEuWNQFuwiJQ506FrV6itpSYlrenaRGxZB0UFWIYNIXDP7XjPPEGPezJkCP7HbkbJyMCWmozNaibj47dxrF8NQF1U9H7BxcR+cJh0BUU3nM49t9lYMrLT/jH8+Y6Yw8OYdjEw+G3yt1SIzxP4/1Wyd9rlQBHjYZD39DgJqYoE9xHxSVSUvuwSRN58U/KOP14X7O3bzrx5oimKhMLD9OW048bt16fCbm2bs6fuzVIbFSV+h61FMWYwLKxlMWZEhAR7925RjFnWsbNoLpd8cclV4vH6JLR9u2gxsVLXRW979kuny/yt7zUdE4iJkvKeHVusq7xH2xb7FYgK/9WMs94umS3WJe1T9GuXk9M8HZabK1pkxMF1NU7VVHTuqU+5nHuuXs+gQSJWqxR07q5PqZxzjoiIrFz+TfO0y966JkwQv9stwf79RbPZJNR4LUM2W5NQVIuKkkBYmIjLJd4HrxV55BH92F69RFNVkYEDRT75ROTKK3Vhb+O51GTlSMjhlIpxz0gwIlICkVHiSU7T7xVVEU1BAgmRkrf8Ccn//BYJWUxS07ad+BPTpG7QyVL48Wuy86arpLhttmzueowseeAB2X7GGVLQJlO+nTJLpr/7sRR06yy7JzwnlXddITWjh4p37BmNy2JV0RSkbtgw0VRVAg7nQSLToMl08FJbRZFQo0g1lJAo0q+fSOvWh/3b+CdyxASnTzzxBPfccw+33HILL7zwAhUVFTz44INMnz6dXbt2ERcXx+mnn87YsWOJjIz8Y6wlAwMD/N4QVvsfoBcfPBgmToRPP4XZs2H48EM7Li8P6dWLBDWEosl+KxClsrL572uvoxVCwGZnwYgzsQ44gch3PqSgzzH0iXSz/pObSJ+5jrjnn2TLsHDsq7fRuroSJUKhIdxB/StPodms7Jr4Cj1rqsGi4osIp+GJ/7DLXgCAc/UW2noa8JSVYXY68f7nP5QU5mPbvIXk+npKa+pxhYVTPfYxlvlNRK1fy4CqKhoiE3FERNNvyzYKPv6KVrdco9e3fgeh+EiOeXg6WyWJ+f3rCV+5jc7VNWheK55IO3VP383mqM1ErNxO5+pKHB4NrTHImDd2PQDqyh04ausp8VTidLuoePohFkdVE7NyI4Orq6hxx+EOD8f/6GNUFZeQdP11KPX1aDUWVKsJf6KK56IBRJ5yMsGOqaj+BpSVq5CoWJRzz6f+mrsg5CEsEKDG40eNjsM69Qe+GXIex5VUkxgI4K+tJagoKF9/w9czZuE0BUj76WWi6urx1FdiinBR9/LTVOfksOH2OznhnNGYvV4Cu/fQMHAgwVdfpb57d1Jra6lJbkdYXRHmmfPgxlOhUypsXI+YVZg/H9/uAmy7cim8+S7ic7qCqiL19UgwRNiTj0MwBDnZKCeMgHEPQ5u2KKWlmGv8pE8XGHgWEvsF3u/fI9zVGktWFuZbz4LUaCy5m3A7I7BO9UNJCbhcnHrZudC2LaFtO1Bjs1Ce+Jfu5bn8NkhYAAMHUlyfT8KPPyFWK+ZQECIi9FD79fUgouf9AUTRl9j6I2xYavyE4uIwl5WinHQivPeeXm9j3hqDP4jfa+EsWbJEMjIyJCcnR2655RYREVm7dq2ceeaZ8t1338m2bdtk1qxZkpmZKaNHj/5TLCcDg38qeWv/gOV+jctUm+jV69CXEebmSklmRwk47BJSVNEURQKxcaKB1PbvqS9JNZtFOneWPceO1JdY9urV3M6UKeKPi5Oy4oL9l2g2CiVrP31RFxfa7XpQqy5dRBRFSp65scUAXP44t/73AQGtfHHxsi13d4uBrqbP2q6XOxwibrfusYmIkFBWW72POTki3bo1tSFJCSLLxrdYl/z8VIv98sZFttgvLTlZpv48d/9gZWaziMUimoLuDVJVkXvu0fuWmyuebpmiRUWJiEiwdQcJtu8k3lfGi9jtMvuDb/S6rFaRm27S6zxQMBkTo3tR5s2TQHRzvwI2m/hHnqC3Mfg4CYaHi6xZo1+DxrGSlBTZ9NnP0rBkgYRsNqmd9LEuHh05Ujx9uohERurX6vLLRfr21beNHi2rn/tANLdbvBlZEhwyTILde0rd2Md1r1coJDJjhn6e+1yXwnWNy6nT0vSAbvPmiSQkSDDCLdK7t+5VO/vsJo/Yrn79pC67rayb/KaEnHbRkpJELBaR8eP173s9GmZLy6JSu10Cdof445JEc7tFIiLEv2mTeDp30kWmInpwuMcfP8wf2D+PP11wWldXx4UXXsjbb79NVFRUU3nnzp2ZNGkSo0aNom3btgwdOpRHH32UyZMnEwwG/yBzycDgn42nzo/dZfnjK7788pZ1FC2w6LufsIYsNMQmE7Q78MckoTboadmldy/8cZFIKAStW7PpyrshGNQDioE+1z96NOayMqLbtNPfKq+4omWhZHQ0JCXpSxzdbmwrt/x3zceBAa0UBb8/+OvHVFXpuWbi46FvX+jeHWV3gf64PlA/ggLab9R1iP1S9rqK9pZPngxuN0RH4+nQTtcWmEzwxBPQubN+TDDUpMtRzj4dpbIcT76CRMdgs5j1uhISwOHQj1EUQnvTvSsKNDTouUn2nktj+xIZgZabp5eqCv6kJD1RnIh+T+wTSM3hCEeJj0edOoeKvJ14aosoPn+ArmcpLdW9BA0NTWO9Lb+YQFBjcVYPgr/MI7BhA/axD4EIMnCgfi/svWapqeD3E33Rrbr25Z13mrUwWVmoDbq3AotFbwMgI4Mtt9+B3RFJp87DCR5/PFseuRFNNELXXYvi8RKIigZADQaaMvYCiMlMSFHB68Xk9aA67FBVhd9kIvfTjykeNw5mztQ9Hnv1PQZ/HL/Hurnkkkvk1ltvFRGRwYMHN3k+WuLtt9+W2NjYX93u9Xqlurq66bN7927D82Fg8F/Yub5MQsHQUWt/x84q+f7JCeJzhovf4RJNUaTkzFP0pZMmk0h6uoTMJgmG6xqB8o499TfyK67QK2hchlo1bIhUrFymz6c3vmF6164ULTZWAm0zRDtQ8xEbKyGHTX+rP1Bb4W5Z8xGIipba7j1a1FZUdO7ZrCsZOVLXW2Rn696CTp0OPiYmRiSnbcs6jS4ZLWo+/G5Xi/2S2Fgp79JlPw2MWCwijRqEvXoZSUvTPTMDBkjIahUt4gDdx179SptMCTqdsumdCbJ65jypyekqmqKIz6F7QCQ8XF/u6nQ2amGar9eed5+TndWVIrm5EoqOkbqcri32tz5r/0BqK9/+Std1ZGbq4753/3/9S28zIkJftux0SmFsqgRVVR58/E1ZddXNoqmKNHRpKyGLWRqG9ZH8X96Vws/HSUOfbAm6w0Wys0USE5v1LhERopnNorlcUvnWWxKy2yXodounS45oIDVZ2VKX3Ukqe/eV+fNXSunAwVKdlS0FaW3+awh1j9Wu91VVRdLSRIuMlKDNJvUrV0ntzz9L6MUXDY/HYfCnaj4+++wzVqxYwdKlS39z37KyMsaOHcs111zzq/s8/vjjPPzww4fbDQODfyyigWo6egvVvv5+G+0T0ynP7kbc1hX4zS5iv51K/R3DMC8voe65y6n9ZjHaogKiQxG8OeZxum34iditm1iwZjdJK5Zz5syZmFxhWEadQUDT0L76iqJuPVjyw3eMrqwiaI9F8wcIvfIUWK3UzP6IiKw0CtLsxJRo+D94icJGzYelsIzodons/PdYYubOwvnWePZ4LFiLi4lvk8lr141jYN5a4sd/xVJ7WyKL8unXuReLbr6AEVvWYp3yOY4FvyCKgrJjO6HkGPydUgie0gfzu89SJ9tRi8pxZaWw49YhhG81Yf/0LYpivVgKiknumsmqW04iboOfsC8msCNJ9zbY8wtJ7NiBun/dTPYllxG65GKCUdGUjD4Dd+dO/Hj2bfTasxHne19Q3CmZblqQgM2JtaEeMalwwxhq77qYsJc+p/6REfgm1BM+o5i8G28kfP58bK++StXxxxPfpi3LLrgId5Uf7YfZ7Dozgbaq/n5v9nn1cPW1tUiDh2BCArtnLSIiPQ3/o1fjnLUImTaVGd2HEVNcSvcunVHP6IBlVw7Km08RbNiIUlKOJTOZ+eecT2xFAxEffMAWuwNP9W5iO6Xz7K3XM3pzJe4vJrI6qRVxXXsyKDaeHV/+iPnt22n35VyiGkoJWmzc/vhdLBpyBmgCigNSkrEv30K8KQvTdXcAup5p9SX/pt3bT8GNt7PppgfoVVdPSFXxR0URGD+BGlc4lkg3Wx5/jnZ3/YvSQUOoHHEy3UefRNerLsC5cSMVrVoRv2d3030rNHs99v5rDfjQundHWbOGoKJQf889RD78MM60VCQ8m3oRwgYP/vN/VP9EDseq2bVrl8THx8vq1aubyn7N81FdXS19+vSRkSNHit/v/9U6Dc+HgcGho2na/z+42P+D0t01sm5uvv6WfMIJ4h10jP5mrKoiUWG6buKHH6SytkI86ckSNJtlU7feUpLTbf9gWhaL+N1REnI49fJXXxXtscdk27g3pSY8UpYPPEamjhm8v7bCapXablktBuAKmc3NwdL2aUezWqW6Z399VUZmpngKi6V25k+iWa1S2ilGatuEiT/BLd6enURGjhTN5ZKQzdIcFGyvDqaxrtKevVoMDJbbp9/+5XtX57ijde3IgAH69jVrRLKyRKzWpgBcWmam+CdNkkCUW4Imk4RsDl3XEB8vwZFDdS3FnXdKMNwlmsmkf//hh2Y9hsUiIZNJvHan+C1WWfDYayKvvCICEnSFNWs+FEVfDTNvnoQsll+9Xr4enfXyjAzd29O3r0hkpARdtv0DpjV6sBqi3PuXz5sn3uhYkdhY8WdmiIDUt0sVGT1aqjLaiLRqJSGTSapuv1PXqVgsuk6lUUOytVM3KcrOkUWX3yA1cQny/fuTJBAXL/XuaNF69RIB+f6u62Xrcf1l0htPSNBi1lfotG30Ss2bJ8FIt6wYOkzK2rTs+dBUVUKqKp6EWKnqni6a2Szy5JN6GP5WrZq8HbVz5ojm8x35H9pflMPxfByW8fH1118LICaTqekDiKIoYjKZJBgMiohITU2N9O/fX4YNGyYej+dP67yBwT+NyqJ6qSyqP2rtL5owS/J+nCFbX3hVtOHDm42Pbt1EosNFjj++SVhafMEo8Tvs8soFZ+lubkXRHzTnniuiKBIymZunGBwO/SH4zDMiCQkScEfq9VitIpdd1iTsXPXz+01RLpvc4fPmiS+6cWp3323z5kkwMUnmL8k/qNwfHyNv336zBMKcEjIpErKY9CWiI0dKMNypCz1TUpqFhvPmSSg5WSYtX3VQXZKSIs/lFu5f3vhgLj/uRNHmz9eNsldf1R9u7duLpKTI6i9XyJfX3yV1kZEy77ihUu2OktqYWKkddIJIcrJo4WGihdskdExvXQzat5toOTn6FFWvXk0ZciUhQXL795IFs7+QqogIqXNHy4rb75agxSJVXTrp0xdWqy5cjY/Xxb6xcS1eLy0pUQqXfquXJyeLdO6sn8txx0kg3KEfM26cfm0az3HriCH7l8+bJyGzRSQmRoI2q3jioiVgMcm3U2bJqlPO0I2S2DjZtTlXF5YmJ+vjHxsr0qWLBO123YhonOIpmzlL6mNiZeExx0vdipUi0dFSmNNBn45rNJjKk+IkZDJJwOkQ75bNktezr5THRkllrJ7BVts3nwxIg9MimqJIw/pl4ktL0NvaN2ePiHh37JDqaT8axsdh8KcJTocNG8batWtZtWpV06dXr15ceOGFrFq1CpPJRE1NDcOHD8dqtfLdd99hb8xnYGBg8P+nqqSByHjHUWl7z/JNOPy72bksF1ePTmivvIzszUHbti1cMgw6dWoSRcaNfYGGgV3pmF4FsTHUdenEJx+/RvCrrxARvE67LoRUFGjTRhdKfvAB+P3NQaveew/WrfvNAFx1WdkHb0tNJdCrL8GgdlD5yuRMvMkuqjNjCLRtjXr8cF0k6XKRe1E/XdCYmNgsNGxs37YnXz/HXbvg0Ufhkkv0wF4t9cvlYtvjb6LU1kJtLXzyiS6ivO8+6NcPZ5SZDp2Ow5qSQcxxrfG2jqE4LU4/9vLLITyC4OUXI/2P0cucdgLz5xwc5Kx7dwIWP60tUZhio2iItbOhLg+/U8FqDuni1cREfVm1zwc//oinY06L10vr3Uuve6/YNytLP799xZZ7A6b9WiC11FRK+xxDbbe+zLv6EexDjscsCqc+/gBdZ0+HZ59FCQaJPX0Uod59mDl0NKUduuD3B6iprkf1etn04mNsfujf7PriA7YESjAHfOSsX4Lr2IHw6aeYvT5kzhy46SYUEaIffxosFkweL6UnDCJp/Rosfi+RZWUABLVgU7ZkAewNAeSyS/Defwemy66C1q0hLw/efhv56Sdq58xBq28gYsRwFKv1d/1eDH6D/6+ls++0S3V1tfTt21e6dOki27Ztk8LCwqbPXq/Ib2F4PgwMfp2jNeVSuKNSlr08Q7SYGAkeM0BCPXqKf9K3Ujz9C9FmzmgUFlp1d/Z9t4tWXSjbZ70jPrdLgpntdJFf544SatNafwu1WqW8uy6KbMoa2qqVSFaWaBaLBCIj9JwfI0boy2yjokQsFglYG7OLdumiv/2PHy8SHS0hU2OukL2BsUT0qaGYWKnq0uvgzK4WiwTsVn266Mcf9TfwrCwRRZGgy35w4LOoKD1/jdXaPI3hdDZlO/WGhbd4TMhk1svDwvSpkk8+EbngAr0dZ5hoqiplY+4RLRgUbe5cfQmqsznwmm/213p9jUHZtPAW2tl7/qoqYrPJ5GdGyMQPhokn0iq+jm33F4O++qrInXdKINLdcvCzmBjx5XTQy8e/r3sjjjlGxOmUoNMmwf79RHO5pG7KV1LhqZDyjculPipS/P37ieZyyuuPXy6ffPIf2dKzg3jDnbK9TRcJKYoUdOkmL6zeJVOfeFlWnX2RrBlwgmz6fomU3P+klIy+uGlprkybJgGbTXy3/0saGmrFk91BtGXLxDv4BCnt20sWPfq4bLzsCllw+SVS1aaV/PzGoxI0myTksIuYTBLMbCeP336veJ1hUu2MkPowt2ggfqu1yetRc845uqflmGOar2HjknN/cbEEMzMlmJf3Z/2U/tYctay2K1asYPHixQC0a9duv225ublk7H1DMDAwOGy0kIaqKr+94x9M6apt7FqYS5KzjrrW2RRc9igOt52Ee6/G9cZjBDaswhITzbxnHyd740ai3nifvFNHYKoyUdUth9zurenz/DbYvBklECIQ6cJc7yVq9Rp9ySXoS3F37cJz0vHYiouQoEaDzYLjxx8puOMWwn9ZiDjszB10Bic+eQ87cnLYcfoZ9L/rTryZmZSGFPL+9TTH3X4Bu9/7ivVKW6LXLmVwdTWVMRqm8GgqHn2RtVfeSY+2XQjaHawZ0ZmTHnyOwMXnY6qtp2jMWcR9WMLsdy8mZ04psc8/zdYTErEUlJGYlYI4bKzMTuPY174h5HCwZNw4+t15Jypw02sTOX/jYvq88CLvjLqE8PwCBnXoQp0/SFrPvoR/9zly820UvjiepJ9/weSOZt6rN9B+7S7i336dddd1wbljM63i3Gx95wbazajC8txzaL1fg7lPw+od4HaQ++EPhP/4PVHPvcCuY07DVJRPbLuOBIJQceowombOpOeqWKqP6Y+1/mcqfEJEfCKex58h3xoHHfvjCNlpVVdLQ0UFZpcL34MPUlBRgaW0lJjWGWy76gRa51dj/fpDTJ1aUzH2BlwTp6F89j2LH7+V5FkLCPv0A/LChcg1G+laW8/O6gKi3WEc+/VkNg5PxVYVwNTgx2qzokZGkrRzB7fkpEHOjZD9Ktr27fjG/QuLx0OwuBQ8tfrNNmkSuJxYX3+TPRHhNPTuSfu338a68GfCs9vQ97NP9FD92dmQu5tBj76BBEMQDOmC0q3buOvZx0BVsYSHs6778XSZM5FQdCIBgtiKC7F37qR7gR57TF9G20jD0qWICK5bb9WD7hlLa/9c/nxb6PAwPB8GBi1TtKNaGmqO3PyzpmmyZeJc2fnjUr0gN1eCI06UmnkrdU9Bz556eaPuYf3slfsHDGt8oyx48FY9WFivXlJ1Qj/974QECXVtLQG7IprZpC+3DQ8XiY4WzR0htX2P0evaK3hsfDOe//yHunahVy+93c6ddd3B0BOb+7SPSDSUlCzfzdqxX2ZXGTlSakecKKvGvdBc15tvilx4oa7FWP3JwefR2P7uBy5v9nyoqi7iNJnk86nzDzpmfue+8nOHnuLfukUkIkKka1f9ev3rXyIREbJ67vstBlgrW/38/uWN11iLi5VQZdnBx1gsEoqMbhaPduvWFBhs2bQFLQdli46WUDB4cPAzq1Xq2iWJP7utLjgdNaq5fZBgv34HC25TUuSzty6XJZMfkaDdKr98+x+Zf+/l0hCfKJ8t3y2yYIHufdnb/r/+JcGwcNm0u1ICuTulISZeKpPSxX/8CSJ9+sjOC85t1o/sc3/lff2y7HxkrIQsFglZDg4Ytndc9no6Sjr2Fl9Khu6NM5kkZLVK0GFv7kejnidYWyfV338v/qLiP+mX9M/ByGprYPA3xFPnxxF+ZOafGwpLWfPOjyT2yaLV8F5N5eqSxThvuELXXzz2WPMBpaVk3HSZXr5PwDC/tzG4oM+HpyCXsNlLCQX9eJNM7BYvpoCiv7Xm5qLZrUhtDQQ17OvX6XV98gl4PLBwIcyZQ597rtE1DBUVMHQo/PvfsGQJKcsXNffpgGBpFrtpv8yuofnzcfzyC50fuUsPxnX55Xp9y5dDSQltL33ooPNg8WKYM4fkJz7Sg3+B3o/Gf4fccf1Bx/RMiqRvwTbMl14GXi/06KHvf+aZ4PXS9obHDm6ntJSIS186uBygqhpl5MnN255/DArXQHQ0oZy+zdlj09ObAoM5bI5fDX4modDBQdliYqi8/UTyrzuOUH0d5OY27R8Kc+E7/ni9nfp6vZ3GhH+DCyPonXEGprgEBmxwkWlzYw16Cd+9GMLDITm5uY2bb8Y7eAgbl66nYccuHEnxhPXuzqLL/gNpabiXr9Q9E/sk4qNfPyKKa0jp2QPVZkMVQTGZ9gsaBjTpOsx2O3EFWzGXFenj9M23qOPHYzp2UHM/xozBe+qpNCxbSvjIkVgS4jE4ghwBY+iwMDwfBgYtcyT0HpqmydJ3vpJZ/35GZn/4oUyZs6Dp89Onk2RPvwFSsmKTrl3YG3q60cOx/qflTeUBn0++e2GiTH58onx91akStJqlsGitlL3wvDREJ0itM0KWpncUn8uuJxhz6cHKtO7dRdq3l5o+g5vbeOQR3cMhImtufUjXZojo26+7TmT0aHlxad7+fWrsVygmVioO0HxUt+8sK28aK5tOOreprqqicqm84VbxtWsv82a/dtD5eU4cKgF/vRRfcYquF0hJ0feJiBCx2eTrz6a22H7+wMES8PtbvF7Lf3q5xfKy1c+3WFdw8EAJVZQ1b1s7UWT6WyKjR0v1rJ37e6P2Bgbr3E33bERENC+tHT9eAhERovXvf5DmI3T66bJ+ypT962rcVj1sqBTk5erbunRpDv4WHS0hu63FwGR1XbNbDL7mjXLLjow24rdapebOZ0RGj5a5H80RLTZWGhITRLNYJPjlpxIo3CnexdNFi4kSf/tU0ex20RwO0fYmhFOUlpfSNi6n3XjRleLv3Ln5WjZ6xTS/X6qnTxfv9h2H8rMwOESOmubDwMDgz8HvDWKxmf7UNrbOXsn6z77hmHuvI/7KM/hm3jxOP7Z/8w55eVR8GM3nDWZadxxE/3FPsnzkhZga6smq9vBt/Ta2tnYyaOxE5nVPo1yKsaZEEUruys68PcwsXoCSCGe7wlj3xMdM27SV7Jnv4LfZKbn5XlJvuxJveCyq2cns028nLjadLl89yuKrx9AxJoaVc+eyKymGrIICZkyeQiAllSHTp9OQmEjVllVwwfnw3HN4b/0XM+ogsaSaTv0H8Ppp9zNy+zwi3vuanSedT8f4VDy9T6KizzG0GncTu76dQ0VQ6HzJZSi524muj2NDlp3W4yay64QuqA0enPm7mfrxyyxpO5g3vFMJNXhZHdaarmYr5rp62rz+Kguvu54uX0xiY5scguER2EqLiayo4dOpC7FFZXD8pEeY220QZk8DXYvKWbQTyLGRNe5Ltp2QjamhgZSqfGbt2EJyWxNdx37C+qGRhNxh2ArKSQ6UsGVdEY7MY+k09gk2D7sA1VNH51l3o646DS1/C6UPvIF3xk5MRXuI6tCbd//1AP2XTKb9558y+eU3cBYVMeSGaynJycb88Lm453TAMuFZamzbMRWWEz97BmEblxDcVc6CJ++gaMkXCIKrsJShSxfDiMEEdxaz7pZLSViwko2/TKJLRhK20krk4nMwv/8GdaZ61KJiwrPbMfnas4kqriN20kcszIgAwLNtEyd3yaIipxNqqY/o+T9RccMNRBZsoaJtFqV9OtEmTEGd9DGhWCjZ8DWJvdqz+eGOpE9ScX7wLWq5X08GJ3LwjayqaBYrQbOVVp56LFlZepj0O+6Ayy8n+NRTNAwbRthxx6EaK1mOGobxYWDwF6BkZy2JrSP+lLr3bCpg8zNvEpNkI3XdDOJaPfSr+wZUhV6dE+kd6YIbrmHowilw3nkQ6SC8fStOa98Pbszn1B01cHdj5OK8PPhuA9fkXAM5sGLJOrrv/hGlSydcG+LA5SLsopOg5n6sY8dCr17E9kmh/7AcAjdeTdKCaSTFxpI0aBA/7VqKtW1bTl6/ThcEXnYZkV98QarZylsrd9N2yKlYHnmG9If+Q0ORxprKekac3I4eST2hd2/adbkGElzsToli6NAeUHMDWRsX6XXl5YHDSpilN616pMMNu8hasgvOO49tvieZV7iODHdvalul4g6PoPeofnDrzfD666QkxBJz3slQsZPe5Tvhar2+0s8/5IzB3Qlzh8OtN3F67mr9ek2JwZ0+kB49usENRXRdUqCXu2ciyYMZ2OdcuNFEv+U1cPeNkJdHXvgcWnVPoU2YG268lp7LvtePGTaEH25/nlPzfiFhzRo44WLIE/jWSXxGKr17XA1vvc5FyXGgBMFmxR/uxDXwGsKGOqB3byI6nwPptciwlawfN5xWqyIYtGYN9DmnaQxrB09h1Yu3cOKiKrrNmwcbc0l64VNYvYklTzxJnztuh969cXUbpC8t/nomkam9GXHhsdC7Nz1jMvXpH2siWvS3LB85gPSTr9Bz+wzvS3RtLSz8ES27O9brr4PevTF3OQFz0TYsYSGw9KHoWBNtXvkYEQhYrVh9fhSap1sAFE3DZ3WhdGiPc+c2mDYNPv8ceest6gcOxHTsICK6dP4zfkoGh4FhfBgY/AWor/Jhtv6xno/8Lfnkrs4lPs1Navc46hYvossPPzQnPTuQjAwWvjO+KXQ3Y8Y0b5s4kbr1Mw4ubzyOiRMBCAaDLO/nQc3sjJSV6nEi7rhj/+NqapoeJjOPbU9RhwjSjzsHpwg14WGwcmVz3fffD/ffjzr9J87pmIT7qfubNr22qJ6zIx3EJUXqBZdfDj/+CBMnYpq67eC+7u3njJ0Hbdv6n5fot+kbrGFtcOftPKj96o+mEtNCfZvuf4zue9/OD7heypzVLZaz5PMW+7bo9ScYpB5QV15e8z7n656fpuvJPnoIvx969oSyMj1J32fjaXpk79XInHceiqIgaPvX1ZhG3mqyUO2r1rc98YSuPXn5ZTj3XGL2jsk+de3H3vLGFSSqyYTsTTR40DEH9CtD/2qjDZEmhWBCMqaiQurTM1C3b8EcDCGKgiqCLyEZW0khtkgnDfExcNKJ8PbbBK+6ioalS3F164YpPByDo48i0pLf6uhRU1NDZGQk1dXVRET8OW96BgZ/NYrzavDVB/7QOneuXUhah6Rf3b49CGrrzCZDQAEKfQFcZpUI08GGUE39ZmJMvqbvCkpTEDJlH1lgHKXE2KPx1qkE67JbbLu4JpeoWN1oqGyoxJJkoSHQgFpaT6L+mN+/rx4/pvad9iubsq6QU5OiW6w/ocpPhtvZ4jZPjZ9axwHvZSGNhtVb0dLiCYuLOugYdfMK3HF7g781/5daWetle0ZnFPPB73lKXSlJ4ftnJ1YUhfX164mNjUWQpusmCJV+LyXmLpyaEE2ctfG4vDy44w6+u/15Tu2fphsWNTVND/nPVuzhvOigbkRMnKgvIV2zhhljzqRfQifCrftcg8a6fhh7PCdmX7d/XXl5aLffznsPnshVOVfB2LHwxRewdi0AVR064J4/v8lQ2csPP8/jxMHHtnidF3/zBn1Pv+6g8tLX3yDu+ubyPXNfJ2Xgteye/jGFUxYys3Uqtz/6OD9cfSoj3vgGm8eHqGbMqkrA5sQS5oBgkLJbbyXqxhvxbdqEBIK4+vVtsR8GfxyH8/w2PB8GBn8BEjL+eEO8VadT/uv2jMOtMKb3Ye7/65vSf21jSsvFrVsoGzL4910zO3CweQHkxP36QZ1OaLE4DEj71YNarq8d7VosB9hY5yG07+tio7dm5RfjMfvboHTugojAz3NRFIUqRWVCVQkD6sqZtmUR9GzN2Y89wpKhSfSNbwc4D6qLjW/o3w/wvNR8/C4/Tr8aT9DDTRdfDKtXN23OP/Ek3Pt4Ng4Fhf09bKJpiN+P+HwESkoQnw/xetEqKqmsXESwjYbljP6cXr0Ec+dElG4ReDok4iwP4vOECEVE47M5IEzXcYRt3Ej5e+8Tdc7ZWBISDrlfBkcGw/NhYGBg8BdhY52HKIuZRNv+HpNXN07nhuzhLR/U6NGQL7/Uw4y/9ir5O+fhfuhpnJZwRAJoWqDp3882f0GvVqfiD/kJasH9qiqZeR/r4tpyXM5lqEpzpIayTT66JXY9qOndpQUomTUtesAS1xWR4IynaXJIUVAsFgTBHBGBYrej2u0oTgdKmANFAcSM2oLXzeB/g8N5fhvGh4GBgcFfhCJfgGXV9bhM+4doKqlaSord9l+PVVAwq2ZMigmTBGjttGNS7SiqGVWxoKpWFMWMR8xYTXYsqgWzav51DZCBwQEY0y4GBgYGf0MSbRZOiXcfvCFm2B/Wxn83YQwM/hiMCKcGBgYGBgYGRxTD+DAwMDAwMDA4ohjGh4GBgYGBgcERxTA+DAwMDAwMDI4ohvFhYGBgYGBgcEQxjA8DAwMDAwODI4phfBgYGBgYGBgcUQzjw8DAwMDAwOCIYhgfBgYGBgYGBkcUw/gwMDAwMDAwOKIYxoeBgYGBgYHBEcUwPgwMDAwMDAyOKIbxYWBgYGBgYHBEMYwPAwMDAwMDgyOK+Wh34EBEBICampqj3BMDAwMDAwODQ2Xvc3vvc/y/8T9nfNTW1gKQlpZ2lHtiYGBgYGBgcLjU1tYSGRn5X/dR5FBMlCOIpmkUFBQQHh6OoihHuzuHTE1NDWlpaezevZuIiIij3R2D38AYr78expj99TDG7K/F/3e8RITa2lqSk5NR1f+u6vif83yoqkpqaurR7sbvJiIiwviR/YUwxuuvhzFmfz2MMftr8f8Zr9/yeOzFEJwaGBgYGBgYHFEM48PAwMDAwMDgiGIYH38QNpuNBx98EJvNdrS7YnAIGOP118MYs78expj9tTiS4/U/Jzg1MDAwMDAw+HtjeD4MDAwMDAwMjiiG8WFgYGBgYGBwRDGMDwMDAwMDA4MjimF8GBgYGBgYGBxRDOPj/8mKFSs44YQTcLvdxMTEcM0111BXV7ffPrt27eLkk0/G6XQSHx/Pv//9b4LB4FHqscGWLVs47bTTiI2NJSIigoEDBzJ79uz99lm6dCnDhg3D7XYTFRXFiBEjWL169VHqscGhjBnA+PHjycnJwW63Ex8fzw033HAUemsAhz5mAOXl5aSmpqIoClVVVUe2owbAb4/X6tWrOf/880lLS8PhcJCdnc2LL774u9szjI//BwUFBRx//PG0a9eOxYsXM23aNNavX89ll13WtE8oFOLkk0/G7/ezYMECJkyYwPjx43nggQeOXsf/4ZxyyikEg0F++uknli9fTteuXTnllFMoKioCoK6ujpEjR9KqVSsWL17ML7/8Qnh4OCNGjCAQCBzl3v8z+a0xA3juuee47777uPvuu1m/fj0zZ85kxIgRR7HX/2wOZcz2cuWVV5KTk3MUemmwl98ar+XLlxMfH89HH33E+vXrue+++7jnnnt45ZVXfl+DYvC7efPNNyU+Pl5CoVBT2Zo1awSQrVu3iojI1KlTRVVVKSoqatrn9ddfl4iICPH5fEe8z/90SktLBZC5c+c2ldXU1AggM2bMEBGRpUuXCiC7du1q2ufAcTU4chzKmFVUVIjD4ZCZM2cerW4a7MOhjNleXnvtNRk8eLDMmjVLAKmsrDzCvTU4nPHalzFjxsiQIUN+V5uG5+P/gc/nw2q17pdAx+FwAPDLL78AsHDhQrp06UJCQkLTPiNGjKCmpob169cf2Q4bEBMTQ1ZWFh988AH19fUEg0HefPNN4uPj6dmzJwBZWVnExMTw7rvv4vf78Xg8vPvuu2RnZ5ORkXF0T+AfyKGM2YwZM9A0jfz8fLKzs0lNTeWcc85h9+7dR7n3/0wOZcwANmzYwCOPPMIHH3zwm4nIDP48DnW8DqS6upro6Ojf1+jvMlkMRERk3bp1Yjab5amnnhKfzycVFRUyevRoAeSxxx4TEZGrr75ahg8fvt9x9fX1AsjUqVOPRrf/8ezevVt69uwpiqKIyWSSpKQkWbFixX77rF27Vtq2bSuqqoqqqpKVlSV5eXlHqccGvzVmjz/+uFgsFsnKypJp06bJwoULZdiwYZKVlWV4GI8SvzVmXq9XcnJy5MMPPxQRkdmzZxuej6PIofy/uC/z588Xs9ksP/744+9qzzA1W+Duu+9GUZT/+tm0aROdOnViwoQJPPvsszidThITE2ndujUJCQmGFX+EOdQxExFuuOEG4uPjmTdvHkuWLOH0009n1KhRFBYWAuDxeLjyyisZMGAAixYtYv78+XTu3JmTTz4Zj8dzlM/078MfOWaaphEIBHjppZcYMWIE/fr149NPP2Xr1q2/KnI0OHz+yDG75557yM7O5qKLLjrKZ/X35Y8cr31Zt24dp512Gg8++CDDhw//XX0zwqu3QGlpKeXl5f91nzZt2mC1Wpu+FxcX43K5UBSFiIgIPvvsM84++2weeOABvvvuO1atWtW0b25uLm3atGHFihV07979zzqNfxSHOmbz5s1j+PDhVFZW7pcyOjMzkyuvvJK7776bd999l3vvvZfCwsImI9Lv9xMVFcW7777Leeed96eeyz+FP3LM3n//fa644gp2795Nampq0z4JCQmMGzeOq6+++k87j38Sf+SYdevWjbVr16IoCgAigqZpmEwm7rvvPh5++OE/9Vz+CfyR47WXDRs2MGTIEK666ioeffTR39038+8+8m9MXFwccXFxh3XMXk3He++9h91u54QTTgCgf//+PProo5SUlBAfHw/o89MRERF07Njxj+34P5hDHbOGhgaAgzxTqqqiaVrTPqqqNv2nuHe7oihN+xj8//kjx2zAgAEAbN68ucn4qKiooKysjPT09D+y2/9o/sgxmzRp0n6exKVLl3LFFVcwb9482rZt+wf2+p/LHzleAOvXr2fo0KFceuml/y/DAzA0H/9fXn75ZVm+fLls3rxZXnnlFXE4HPLiiy82bQ8Gg9K5c2cZPny4rFq1SqZNmyZxcXFyzz33HMVe/3MpLS2VmJgYOfPMM2XVqlWyefNmueOOO8RisciqVatERGTjxo1is9nk+uuvlw0bNsi6devkoosuksjISCkoKDjKZ/DP41DGTETktNNOk06dOsn8+fNl7dq1csopp0jHjh3F7/cfxd7/MznUMdsXQ/Nx9DiU8Vq7dq3ExcXJRRddJIWFhU2fkpKS39WmYXz8P7n44oslOjparFar5OTkyAcffHDQPnl5eXLiiSeKw+GQ2NhYuf322yUQCByF3hqI6Etphw8fLtHR0RIeHi79+vU7SPw7ffp0GTBggERGRkpUVJQMHTpUFi5ceJR6bHAoY1ZdXS1XXHGFuN1uiY6OljPOOGO/5dIGR5ZDGbN9MYyPo8tvjdeDDz4owEGf9PT039WeofkwMDAwMDAwOKIYSzIMDAwMDAwMjiiG8WFgYGBgYGBwRDGMDwMDAwMDA4MjimF8GBgYGBgYGBxRDOPDwMDAwMDA4IhiGB8GBgYGBgYGRxTD+DAwMDAwMDA4ohjGh4GBgYGBgcERxTA+DAwMDAwMDI4ohvFhYGBgYGBgcEQxjA8DAwMDAwODI4phfBgYGBgYGBgcUf4PAmpkxJzu/L4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Rep-leaning vtds for year\",years[0])  #for MA, do GOP-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] < RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"R\", color = \"red\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 2804034 2649859 -154174\n",
      "ensemble : 2799312 2649231 -150080\n",
      "the true and ensemble state GOP leans are 0.48587 0.4862274274679837\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 4806 4805 13\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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